CN103675011A - Soft industrial melt index measurement instrument and method of optimal support vector machine - Google Patents

Abstract

The invention discloses a soft industrial melt index measurement instrument and method of an optimal support vector machine. The soft measurement method comprises the following steps: carrying out fuzzy processing on outputs of a plurality of weighting least square support vector machines; and optimizing a whole fuzzy equation system by adopting a particle swarm optimization to obtain an optimal soft measurement result. According to the soft industrial melt index measurement instrument and method of the optimal support vector machine, a field intelligent instrument for measuring easily-measured variables and a control station for measuring operation variables are connected with a DCS (Data Communication System) database; a soft measurement value displayer comprises a soft industrial melt index measurement model of the optimal support vector machine; the DCS database is connected with the input end of the soft measurement model; the output end of the soft industrial melt index measurement model of the optimal support vector machine is connected with the soft melt index measurement value displayer; the soft industrial melt index measurement instrument and method of the optimal support vector machine have the characteristics of on-line parameter optimization, strong noise immunity and good popularization performance, and the automatic updating of the model can be realized.

Description

Industrial melting index soft measuring instrument and the method for optimum support vector machine

Technical field

The present invention relates to soft measuring instrument and method, relate in particular to a kind of industrial melting index soft measuring instrument and method of optimum support vector machine.

Background technology

Polypropylene is a kind of hemicrystalline thermoplastics being formed by propylene polymerization, has higher resistance to impact, and engineering properties is tough, and anti-multiple organic solvent and acid and alkali corrosion, be widely used in industry member, is one of usual modal macromolecular material.Melting index (MI) is to determine one of important quality index of the final products trade mark during polypropylene is produced, and it has determined the different purposes of product.Measuring accurately, timely of melting index, to producing and scientific research, has very important effect and directive significance.Yet the on-line analysis of melting index is measured and is still difficult at present accomplish, the in-line analyzer that lacks melting index is a subject matter of restriction polypropylene product quality.MI can only obtain by hand sampling, off-line assay, and analyzes once for general every 2-4 hour, and time lag is large, is difficult to meet the requirement of producing real-time control.

Research work major part about the online forecasting of MI all concentrates on above artificial neural network in recent years, has obtained good effect.But artificial neural network also has the shortcoming of himself, for example the interstitial content of over-fitting, hidden layer and parameter are bad determines.Secondly, the DCS data that industry spot collects also because noise, manual operation error etc. with certain uncertain error, so use the general Generalization Ability of forecasting model of the artificial neural network that determinacy is strong or not.

First nineteen sixty-five U.S. mathematician L.Zadeh has proposed the concept of fuzzy set.Fuzzy logic, in the mode of its problem closer to daily people and meaning of one's words statement, starts to replace adhering to the classical logic that all things can represent with binary item subsequently.Fuzzy logic so far successful Application industry a plurality of fields among, fields such as household electrical appliances, Industry Control.2003, Demirci proposed the concept of fuzzifying equation, and by using fuzzy membership matrix and building a new input matrix with its distortion, the gravity model appoach of then usining in local equation in Anti-fuzzy method show that analytic value is as last output.For the soft measurement of melting index in propylene polymerization production process, consider noise effect and operate miss in industrial processes, can use the fuzzy performance of fuzzy logic to reduce the impact of error on whole forecast precision.

Support vector machine, is introduced in 1998 by Vapnik, due to its good Generalization Ability, is widely used in pattern-recognition, matching and classification problem.Because standard support vector machine is responsive to isolated point and noise, so proposed again afterwards Weighted Least Squares Support Vector Machines.Weighted Least Squares Support Vector Machines can be processed the sample data with noise better than standard support vector machine, is selected as the local equation in fuzzifying equation here.

Particle cluster algorithm, Particle Swarm Optimization, is a kind of a kind of biological intelligence optimizing algorithm of seeking global optimum by imitating Bird Flight behavior being put forward by Kennedy and professor Eberhart, is called for short PSO.This algorithm, by interparticle influencing each other in colony, has reduced searching algorithm and has been absorbed in the risk of locally optimal solution, has good global search performance.Particle cluster algorithm is used to the best parameter group of search weighted least square method supporting vector machine, to reach the object of Optimized model.

Summary of the invention

In order to overcome the deficiency that the measuring accuracy of existing propylene polymerization production process is not high, low to noise sensitivity, promote poor performance, the invention provides a kind of on-line measurement, computing velocity is fast, model upgrades automatically, noise resisting ability strong, promote industrial melting index soft measuring instrument and the method for the optimum support vector machine that performance is good.

A kind of industrial melting index soft measuring instrument of optimum support vector machine, comprise propylene polymerization production process, for measuring the field intelligent instrument of easy survey variable, for measuring the control station of performance variable, the DCS database of store data and melt index flexible measured value display instrument, described field intelligent instrument, control station is connected with propylene polymerization production process, described field intelligent instrument, control station is connected with DCS database, described soft measuring instrument also comprises the industrial melting index soft-sensing model of optimum support vector machine, described DCS database is connected with the input end of the industrial melting index soft-sensing model of described optimum support vector machine, the output terminal of the industrial melting index soft-sensing model of described optimum support vector machine is connected with melt index flexible measured value display instrument, the industrial melting index soft-sensing model of described optimum support vector machine comprises:

Data preprocessing module, for by carrying out pre-service from the model training sample of DCS database input, to training sample centralization, deducts the mean value of sample, then it is carried out to standardization:

Computation of mean values: $\stackrel{\‾}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{TX}}_i---\left(1\right)$

Calculate variance: ${\mathrm{\σ}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}({\mathrm{TX}}_i-\stackrel{\‾}{\mathrm{TX}})---\left(2\right)$

Standardization: $X=\frac{\mathrm{TX}-\stackrel{\‾}{\mathrm{TX}}}{{\mathrm{\σ}}_x}---\left(3\right)$

Wherein, TX _ibe i training sample, N is number of training,

for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that represents training sample, σ ² _xthe variance that represents training sample.

Fuzzifying equation module, the training sample X to from data preprocessing module passes the standardization of coming, carries out obfuscation.If have c in fuzzifying equation system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, i training sample X after standardization _idegree of membership μ for fuzzy group k _ikfor:

${\mathrm{\μ}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\Σ}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needing in fuzzy classification process, conventionally get and do 2, || || be norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)=[1func(μ _ik)X _i] （5）

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get

exp (μ _ik) etc., Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Weighted Least Squares Support Vector Machines, as the local equation of fuzzifying equation system, is optimized matching to each fuzzy group.If i target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\ξ})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\γ}{\mathrm{\Σ}}_{i=1}^N{\mathrm{\ω}}_i{\mathrm{\ξ}}_i^2---\left(6\right)$

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, and N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ibe i component of slack variable, w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ are respectively weight and the penalty factors of Weighted Least Squares Support Vector Machines,

i component ξ of Weighted Least Squares Support Vector Machines slack variable _ithe estimation of standard deviation, c ₁for constant, get 2.5, c here ₂for constant, get 3 here, the transposition of subscript T representing matrix, μ _ikrepresent i training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.By (6) (7) (8) formula, can derive fuzzy group k is output as at training sample i:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;K<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein,

for the output of fuzzy group k at training sample i.K<> is the kernel function of Weighted Least Squares Support Vector Machines, and K<> gets linear kernel function here; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) m input variable X of expression _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in Anti-fuzzy method obtains the output of last fuzzifying equation system:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

Wherein,

for the output of fuzzy group k at training sample i.

Particle cluster algorithm is optimized module, and for adopting particle cluster algorithm to be optimized the penalty factor of fuzzifying equation Weighted Least Squares Support Vector Machines local equation and error margin value, specific implementation step is as follows:

7. the Optimal Parameters of determining population is the penalty factor of Weighted Least Squares Support Vector Machines local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

8. set optimization aim function, be converted into fitness, each On Local Fuzzy equation is evaluated; By corresponding error function, calculate fitness function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzifying equation system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula,

the prediction output of fuzzifying equation system, O _itarget output for fuzzifying equation system;

9. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _prepresent the more speed of new particle p, r _prepresent the more position of new particle p, Lbest _prepresent the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter represents cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

10. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

if the individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

judge whether to meet performance requirement, if so, finish optimizing, obtain the local equation parameter of one group of fuzzifying equation of optimizing; Otherwise return to step 3., continue iteration optimizing, until reach maximum iteration time iter _max.

As preferred a kind of scheme, the industrial melting index soft-sensing model of described optimum support vector machine also comprises: model modification module, for the online updating of model, regularly off-line analysis data is input in training set, and upgrade fuzzifying equation model.

An industrial melt index flexible measurement method for optimum support vector machine, described flexible measurement method specific implementation step is as follows:

1), to propylene polymerization production process object, according to industrial analysis and Operations Analyst, select performance variable and easily survey variable as the input of model, performance variable and easily survey variable and obtained by DCS database;

2), the model training sample from DCS database input is carried out to pre-service, to training sample centralization, deduct the mean value of sample, then it is carried out to standardization, making its average is 0, and variance is 1.This processing adopts following formula process to complete:

2.1) computation of mean values: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

2.2) calculate variance: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

2.3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Wherein, TX _ibe i training sample, N is number of training,

for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that represents training sample, σ ² _xthe variance that represents training sample.

3), to pass the training sample come from data preprocessing module, carry out obfuscation.If have c in fuzzifying equation system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, i training sample X after standardization _idegree of membership μ for fuzzy group k _ikfor:

${\mathrm{\&mu;}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\&Sigma;}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needing in fuzzy classification process, conventionally get and do 2, || || be norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)=[1func(μ _ik)X _i] （5）

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get exp (μ _ik) etc., Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Weighted Least Squares Support Vector Machines, as the local equation of fuzzifying equation system, is optimized matching to each fuzzy group.If i target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\&xi;})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\&gamma;}{\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&omega;}}_i{\mathrm{\&xi;}}_i^2---\left(6\right)$

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, and N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ibe i component of slack variable, w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ are respectively weight and the penalty factors of Weighted Least Squares Support Vector Machines,

i component ξ of Weighted Least Squares Support Vector Machines slack variable _ithe estimation of standard deviation, c ₁for constant, get 2.5, c here ₂for constant, get 3 here, the transposition of subscript T representing matrix, μ _ikrepresent i training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.By (6) (7) (8) formula, can derive fuzzy group k is output as at training sample i:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;K<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein,

for the output of fuzzy group k at training sample i.K<> is the kernel function of Weighted Least Squares Support Vector Machines, and K<> gets linear kernel function here; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) m input variable X of expression _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in Anti-fuzzy method obtains the output of last fuzzifying equation system:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

Wherein, for the output of fuzzy group k at training sample i.

4), adopt particle cluster algorithm to be optimized the penalty factor of Weighted Least Squares Support Vector Machines local equation in fuzzifying equation and error margin value, specific implementation step is as follows:

7. the Optimal Parameters of determining population is the penalty factor of Weighted Least Squares Support Vector Machines local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

8. set optimization aim function, be converted into fitness, each On Local Fuzzy equation is evaluated; By corresponding error function, calculate fitness function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzifying equation system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula, the prediction output of fuzzifying equation system, O _itarget output for fuzzifying equation system;

9. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _prepresent the more speed of new particle p, r _prepresent the more position of new particle p, Lbest _prepresent the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter represents cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

10. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

if the individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

judge whether to meet performance requirement, if so, finish optimizing, obtain the local equation parameter of one group of fuzzifying equation of optimizing; Otherwise return to step 3., continue iteration optimizing, until reach maximum iteration time iter _max.

As preferred a kind of scheme: described flexible measurement method is further comprising the steps of: 5), regularly off-line analysis data is input in training set, upgrade fuzzifying equation model.

Technical conceive of the present invention is: the important quality index melting index to propylene polymerization production process is carried out online soft sensor, overcome the deficiency that existing polypropylene melting index measurement instrument measuring accuracy is not high, low to noise sensitivity, promote poor performance, introduce particle cluster algorithm fuzzifying equation model is carried out to Automatic Optimal, do not need artificial experience repeatedly to adjust the parameter of Weighted Least Squares Support Vector Machines local equation in fuzzifying equation.This model has following advantage with respect to existing melting index soft-sensing model: (1) has reduced noise and the impact of manual operation error on model prediction precision; (2) strengthened the popularization performance of model, over-fitting has effectively been suppressed; (3) parameter of model is carried out to automatic optimal, improved the stability of model, reduced the possibility that model is absorbed in local optimum.

Beneficial effect of the present invention is mainly manifested in: 1, on-line measurement; 2, on-line parameter Automatic Optimal; 3, model upgrades automatically; 4, anti-noise jamming ability strong, 5, precision is high; 6, Generalization Ability is strong.

Accompanying drawing explanation

Fig. 1 is the industrial melting index soft measuring instrument of optimum support vector machine and the basic structure schematic diagram of method;

Fig. 2 is the industrial melting index soft-sensing model structural representation of optimum support vector machine.

Embodiment

Below in conjunction with accompanying drawing, the invention will be further described.The embodiment of the present invention is used for the present invention that explains, rather than limits the invention, and in the protection domain of spirit of the present invention and claim, any modification and change that the present invention is made, all fall into protection scope of the present invention.

Embodiment 1

With reference to Fig. 1, Fig. 2, a kind of industrial melting index soft measuring instrument of optimum support vector machine, comprise propylene polymerization production process 1, for measuring the field intelligent instrument 2 of easy survey variable, for measuring the control station 3 of performance variable, the DCS database 4 of store data and melt index flexible measured value display instrument 6, described field intelligent instrument 2, control station 3 is connected with propylene polymerization production process 1, described field intelligent instrument 2, control station 3 is connected with DCS database 4, described soft measuring instrument also comprises the soft-sensing model 5 of particle cluster algorithm optimization Weighted Least Squares Support Vector Machines fuzzifying equation, described DCS database 4 is connected with the input end of the industrial melting index soft-sensing model 5 of described optimum support vector machine, the output terminal of the industrial melting index soft-sensing model 5 of described optimum support vector machine is connected with melt index flexible measured value display instrument 6, the industrial melting index soft-sensing model of described optimum support vector machine comprises:

Data preprocessing module, for by carrying out pre-service from the model training sample of DCS database input, to training sample centralization, deducts the mean value of sample, then it is carried out to standardization:

Computation of mean values: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

Calculate variance: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

Standardization: $X=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Wherein, TX _ibe i training sample, N is number of training, for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that represents training sample, σ ² _xthe variance that represents training sample.

Fuzzifying equation module, the training sample X to from data preprocessing module passes the standardization of coming, carries out obfuscation.If have c in fuzzifying equation system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, i training sample X after standardization _idegree of membership μ for fuzzy group k _ikfor:

${\mathrm{\&mu;}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\&Sigma;}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needing in fuzzy classification process, conventionally get and do 2, || || be norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)=[1func(μ _ik)X _i] （5）

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get

exp (μ _ik) etc., Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Weighted Least Squares Support Vector Machines, as the local equation of fuzzifying equation system, is optimized matching to each fuzzy group.If i target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\&xi;})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\&gamma;}{\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&omega;}}_i{\mathrm{\&xi;}}_i^2---\left(6\right)$

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, and N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ibe i component of slack variable, w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ are respectively weight and the penalty factors of Weighted Least Squares Support Vector Machines,

i component ξ of Weighted Least Squares Support Vector Machines slack variable _ithe estimation of standard deviation, c ₁for constant, get 2.5, c here ₂for constant, get 3 here, the transposition of subscript T representing matrix, μ _ikrepresent i training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.By (6) (7) (8) formula, can derive fuzzy group k is output as at training sample i:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;K<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein,

for the output of fuzzy group k at training sample i.K<> is the kernel function of Weighted Least Squares Support Vector Machines, and K<> gets linear kernel function here; α _m, m=1 ..., N is corresponding Lagrange multiplier, μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) m input variable X of expression _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in Anti-fuzzy method obtains the output of last fuzzifying equation system:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

Wherein,

for the output of fuzzy group k at training sample i.

Particle cluster algorithm is optimized module, and for adopting particle cluster algorithm to be optimized the penalty factor of fuzzifying equation Weighted Least Squares Support Vector Machines local equation and error margin value, specific implementation step is as follows:

1. the Optimal Parameters of determining population is the penalty factor of Weighted Least Squares Support Vector Machines local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

2. set optimization aim function, be converted into fitness, each On Local Fuzzy equation is evaluated; By corresponding error function, calculate fitness function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzifying equation system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula,

the prediction output of fuzzifying equation system, O _itarget output for fuzzifying equation system;

3. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _prepresent the more speed of new particle p, r _prepresent the more position of new particle p, Lbest _prepresent the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter represents cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

4. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

If the 5. individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

6. judge whether to meet performance requirement, if so, finish optimizing, obtain the local equation parameter of one group of fuzzifying equation of optimizing; Otherwise return to step 3., continue iteration optimizing, until reach maximum iteration time iter _max.

As preferred a kind of scheme, the industrial melting index soft-sensing model of described optimum support vector machine also comprises: model modification module, for the online updating of model, regularly off-line analysis data is input in training set, and upgrade fuzzifying equation system model.

According to reaction mechanism and flow process analysis, consider the various factors in polypropylene production process, melting index being exerted an influence, get nine performance variables conventional in actual production process and easily survey variable as modeling variable, have: three strand of third rare feed flow rates, major catalyst flow rate, cocatalyst flow rate, temperature in the kettle, pressure, liquid level, hydrogen volume concentration in still.Table 1 has been listed 9 modeling variablees as soft-sensing model 5 inputs, is respectively liquid level (L) in temperature in the kettle (T), still internal pressure (p), still, the interior hydrogen volume concentration (X of still _v), 3 bursts of propylene feed flow rates (first strand of third rare feed flow rates f1, second strand of third rare feed flow rates f2, the 3rd strand of third rare feed flow rates f3), 2 bursts of catalyst charge flow rates (major catalyst flow rate f4, cocatalyst flow rate f5).Polyreaction in reactor is that reaction mass mixes rear participation reaction repeatedly, so mode input variable relates to the mean value in front some moment of process variable employing of material.The mean value of last hour for data acquisition in this example.Melting index off-line laboratory values is as the output variable of soft-sensing model 5.By hand sampling, off-line assay, obtain, within every 4 hours, analyze and gather once.

Field intelligent instrument 2 and control station 3 are connected with propylene polymerization production process 1, are connected with DCS database 4; Soft-sensing model 5 is connected with DCS database and soft measured value display instrument 6.Field intelligent instrument 2 is measured the easy survey variable that propylene polymerization is produced object, will easily survey variable and be transferred to DCS database 4; Control station 3 is controlled the performance variable that propylene polymerization is produced object, and performance variable is transferred to DCS database 4.In DCS database 4, the variable data of record is as the input of the industrial melting index soft-sensing model 5 of optimum support vector machine, and soft measured value display instrument 6 is for showing the output of the industrial melting index soft-sensing model 5 of optimum support vector machine, i.e. soft measured value.

Table 1: the required modeling variable of industrial melting index soft-sensing model of optimum support vector machine

Variable symbol	Variable implication	Variable symbol	Variable implication
				T	Temperature in the kettle	f1	First strand of third rare feed flow rates
p	Pressure in still	f2	Second strand of third rare feed flow rates

[0139]

L	Liquid level in still	f3	The 3rd strand of third rare feed flow rates
				X v	Hydrogen volume concentration in still	f4	Major catalyst flow rate
		f5	Cocatalyst flow rate

The industrial melting index soft-sensing model 5 of optimum support vector machine, comprises following 4 parts:

Data preprocessing module 7, for by carrying out pre-service from the model training sample of DCS database input, to training sample centralization, deducts the mean value of sample, then it is carried out to standardization:

Computation of mean values: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

Calculate variance: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

Standardization: $X=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Wherein, TX _ibe i training sample, N is number of training,

for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that represents training sample, σ ² _xthe variance that represents training sample.

Fuzzifying equation module 8, the training sample X to from data preprocessing module passes the standardization of coming, carries out obfuscation.If have c in fuzzifying equation system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, i training sample X after standardization _idegree of membership μ for fuzzy group k _ikfor:

${\mathrm{\&mu;}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\&Sigma;}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needing in fuzzy classification process, conventionally get and do 2, || || be norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)=[1func(μ _ik)X _i] （5）

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get

exp (μ _ik) etc., Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Weighted Least Squares Support Vector Machines, as the local equation of fuzzifying equation system, is optimized matching to each fuzzy group.If i target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\&xi;})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\&gamma;}{\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&omega;}}_i{\mathrm{\&xi;}}_i^2---\left(6\right)$

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, and N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ibe i component of slack variable, w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ are respectively weight and the penalty factors of Weighted Least Squares Support Vector Machines,

i component ξ of Weighted Least Squares Support Vector Machines slack variable _ithe estimation of standard deviation, c ₁for constant, get 2.5, c here ₂for constant, get 3 here, the transposition of subscript T representing matrix, μ _ikrepresent i training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.By (6) (7) (8) formula, can derive fuzzy group k is output as at training sample i:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;K<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein,

for the output of fuzzy group k at training sample i.K<> is the kernel function of Weighted Least Squares Support Vector Machines, and K<> gets linear kernel function here; α _m, m=1 ..., N is corresponding Lagrange multiplier, μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) m input variable X of expression _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in Anti-fuzzy method obtains the output of last fuzzifying equation system:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

Wherein,

for the output of fuzzy group k at training sample i.

Particle cluster algorithm is optimized module 9, and for adopting particle cluster algorithm to be optimized the penalty factor of fuzzifying equation Weighted Least Squares Support Vector Machines local equation and error margin value, specific implementation step is as follows:

1. the Optimal Parameters of determining population is the penalty factor of Weighted Least Squares Support Vector Machines local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

2. set optimization aim function, be converted into fitness, each On Local Fuzzy equation is evaluated; By corresponding error function, calculate fitness function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzifying equation system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula,

the prediction output of fuzzifying equation system, O _itarget output for fuzzifying equation system;

3. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _prepresent the more speed of new particle p, r _prepresent the more position of new particle p, Lbest _prepresent the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter represents cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

4. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

If the 5. individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

6. judge whether to meet performance requirement, if so, finish optimizing, obtain the local equation parameter of one group of fuzzifying equation of optimizing; Otherwise return to step 3., continue iteration optimizing, until reach maximum iteration time iter _max.

Model modification module 10, for the online updating of model, is regularly input to off-line analysis data in training set, upgrades fuzzifying equation model.

Embodiment 2

With reference to Fig. 1, Fig. 2, a kind of industrial polypropylene producing melt index flexible measurement method of optimizing Weighted Least Squares Support Vector Machines fuzzifying equation model based on particle cluster algorithm, described flexible measurement method concrete methods of realizing is as follows:

1), to propylene polymerization production process object, according to industrial analysis and Operations Analyst, select performance variable and easily survey variable as the input of model, performance variable and easily survey variable and obtained by DCS database;

2), the model training sample from DCS database input is carried out to pre-service, to training sample centralization, deduct the mean value of sample, then it is carried out to standardization, making its average is 0, and variance is 1.This processing adopts following formula process to complete:

2.1) computation of mean values: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

2.2) calculate variance: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

2.3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Wherein, TX _ibe i training sample, N is number of training,

for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that represents training sample, σ ² _xthe variance that represents training sample.

3), to pass the training sample come from data preprocessing module, carry out obfuscation.If have c in fuzzifying equation system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, i training sample X after standardization _idegree of membership μ for fuzzy group k _ikfor:

${\mathrm{\&mu;}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\&Sigma;}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needing in fuzzy classification process, conventionally get and do 2, || || be norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)=[1func(μ _ik)Xi] （5）

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get

exp (μ _ik) etc., Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Weighted Least Squares Support Vector Machines, as the local equation of fuzzifying equation system, is optimized matching to each fuzzy group.If i target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\&xi;})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\&gamma;}{\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&omega;}}_i{\mathrm{\&xi;}}_i^2---\left(6\right)$

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, and N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ibe i component of slack variable, w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ are respectively weight and the penalty factors of Weighted Least Squares Support Vector Machines,

i component ξ of Weighted Least Squares Support Vector Machines slack variable _ithe estimation of standard deviation, c ₁for constant, get 2.5, c here ₂for constant, get 3 here, the transposition of subscript T representing matrix, μ _ikrepresent i training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.By (6) (7) (8) formula, can derive fuzzy group k is output as at training sample i:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;K<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein,

for the output of fuzzy group k at training sample i.K<> is the kernel function of Weighted Least Squares Support Vector Machines, and K<> gets linear kernel function here; α _m, m=1 ..., N is corresponding Lagrange multiplier, μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) m input variable X of expression _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in Anti-fuzzy method obtains the output of last fuzzifying equation system:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

Wherein,

for the output of fuzzy group k at training sample i.

4), adopt particle cluster algorithm to be optimized the penalty factor of Weighted Least Squares Support Vector Machines local equation in fuzzifying equation and error margin value, specific implementation step is as follows:

1. the Optimal Parameters of determining population is the penalty factor of Weighted Least Squares Support Vector Machines local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

2. set optimization aim function, be converted into fitness, each On Local Fuzzy equation is evaluated; By corresponding error function, calculate fitness function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzifying equation system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula,

the prediction output of fuzzifying equation system, O _itarget output for fuzzifying equation system;

3. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _prepresent the more speed of new particle p, r _prepresent the more position of new particle p, Lbest _prepresent the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter represents cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

4. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

If the 5. individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

6. judge whether to meet performance requirement, if so, finish optimizing, obtain the local equation parameter of one group of fuzzifying equation of optimizing; Otherwise return to step 3., continue iteration optimizing, until reach maximum iteration time iter _max.

As preferred a kind of scheme: described flexible measurement method is further comprising the steps of: 4), regularly off-line analysis data is input in training set, upgrade fuzzifying equation model.

The concrete implementation step of method of the present embodiment is as follows:

Step 1: to propylene polymerization production process object 1, according to industrial analysis and Operations Analyst, select performance variable and easily survey variable as the input of model.Performance variable and easily survey variable are obtained by DCS database 4.

Step 2: sample data is carried out to pre-service, completed by data preprocessing module 7.

Step 3: set up initial fuzzy equation model 8 based on model training sample data.Input data obtain as described in step 2, and output data are obtained by off-line chemical examination.

Step 4: the local weighted least square method supporting vector machine equation parameter of being optimized initial fuzzy equation model 8 by particle cluster algorithm 9.

Step 5: model modification module 10 is regularly input to off-line analysis data in training set, upgrades fuzzifying equation model, optimizes the soft-sensing model 5 of Weighted Least Squares Support Vector Machines fuzzifying equation model set up based on particle cluster algorithm.

Step 6: melt index flexible measured value display instrument 6 shows the output of the industrial melting index soft-sensing model 5 of optimum support vector machine, completes the demonstration that industrial polypropylene producing melt index flexible is measured.

Claims (2)

1. the industrial melt index flexible of an optimum support vector machine is measured soft measuring instrument, comprise for measuring the field intelligent instrument of easy survey variable, for measuring the control station of performance variable, the DCS database of store data and melt index flexible measured value display instrument, described field intelligent instrument, control station is connected with DCS database, it is characterized in that: described soft measuring instrument also comprises the industrial melting index soft-sensing model of optimum support vector machine, described DCS database is connected with the input end of the industrial melting index soft-sensing model of described optimum support vector machine, the output terminal of the industrial melting index soft-sensing model of described optimum support vector machine is connected with melt index flexible measured value display instrument, the industrial melting index soft-sensing model of described optimum support vector machine comprises:

Data preprocessing module, for by carrying out pre-service from the model training sample of DCS database input, to training sample centralization, deducts the mean value of sample, then it is carried out to standardization:

Computation of mean values: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

Calculate variance: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

Standardization: $X=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Wherein, TX _ibe i training sample, N is number of training,

for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that represents training sample, σ ² _xthe variance that represents training sample.

Fuzzifying equation module, the training sample X to from data preprocessing module passes the standardization of coming, carries out obfuscation.If have c in fuzzifying equation system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, i training sample X after standardization _idegree of membership μ for fuzzy group k _ikfor:

${\mathrm{\&mu;}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\&Sigma;}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needing in fuzzy classification process, conventionally get and do 2, || || be norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)=[1 func(μ _ik) X _i] （5）

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get

exp (μ _ik) etc., Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Weighted Least Squares Support Vector Machines, as the local equation of fuzzifying equation system, is optimized matching to each fuzzy group.If i target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\&xi;})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\&gamma;}{\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&omega;}}_i{\mathrm{\&xi;}}_i^2---\left(6\right)$

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, and N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ibe i component of slack variable, w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ are respectively weight and the penalty factors of Weighted Least Squares Support Vector Machines,

i component ξ of Weighted Least Squares Support Vector Machines slack variable _ithe estimation of standard deviation, c ₁for constant, get 2.5, c here ₂for constant, get 3 here, the transposition of subscript T representing matrix, μ _ikrepresent i training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.By (6) (7) (8) formula, can derive fuzzy group k is output as at training sample i:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;K<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein, for the output of fuzzy group k at training sample i.K<> is the kernel function of Weighted Least Squares Support Vector Machines, and K<> gets linear kernel function here; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) m input variable X of expression _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in Anti-fuzzy method obtains the output of last fuzzifying equation system:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

Wherein,

for the output of fuzzy group k at training sample i.

Particle cluster algorithm is optimized module, and for adopting particle cluster algorithm to be optimized the penalty factor of fuzzifying equation Weighted Least Squares Support Vector Machines local equation and error margin value, specific implementation step is as follows:

1. the Optimal Parameters of determining population is the penalty factor of Weighted Least Squares Support Vector Machines local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

2. set optimization aim function, be converted into fitness, each On Local Fuzzy equation is evaluated; By corresponding error function, calculate fitness function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzifying equation system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula,

the prediction output of fuzzifying equation system, O _itarget output for fuzzifying equation system;

3. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _prepresent the more speed of new particle p, r _prepresent the more position of new particle p, Lbest _prepresent the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter represents cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

4. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

If the 5. individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

6. judge whether to meet performance requirement, if so, finish optimizing, obtain the local equation parameter of one group of fuzzifying equation of optimizing; Otherwise return to step 3., continue iteration optimizing, until reach maximum iteration time iter _max.

The industrial melting index soft-sensing model of described optimum support vector machine also comprises:

Model modification module, for the online updating of model, is regularly input to off-line analysis data in training set, upgrades fuzzifying equation model.

2. a flexible measurement method of realizing with the industrial melting index soft measuring instrument of optimum support vector machine as claimed in claim 1, is characterized in that: described flexible measurement method specific implementation step is as follows:

1), to propylene polymerization production process object, according to industrial analysis and Operations Analyst, select performance variable and easily survey variable as the input of model, performance variable and easily survey variable and obtained by DCS database;

2), the model training sample from DCS database input is carried out to pre-service, to training sample centralization, deduct the mean value of sample, then it is carried out to standardization, making its average is 0, and variance is 1.This processing adopts following formula process to complete:

2.1) computation of mean values: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

2.2) calculate variance: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

2.3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Wherein, TX _ibe i training sample, N is number of training,

for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that represents training sample, σ ² _xthe variance that represents training sample.

3), to pass the training sample come from data preprocessing module, carry out obfuscation.If have c in fuzzifying equation system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, i training sample X after standardization _idegree of membership μ for fuzzy group k _ikfor:

${\mathrm{\&mu;}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\&Sigma;}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needing in fuzzy classification process, conventionally get and do 2, || || be norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)=[1 func(μ _ik) X _i] （5）

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get

exp (μ _ik) etc., Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Weighted Least Squares Support Vector Machines, as the local equation of fuzzifying equation system, is optimized matching to each fuzzy group.If i target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\&xi;})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\&gamma;}{\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&omega;}}_i{\mathrm{\&xi;}}_i^2---\left(6\right)$

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, and N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ibe i component of slack variable, w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ are respectively weight and the penalty factors of Weighted Least Squares Support Vector Machines,

i component ξ of Weighted Least Squares Support Vector Machines slack variable _ithe estimation of standard deviation, c ₁for constant, get 2.5, c here ₂for constant, get 3 here, the transposition of subscript T representing matrix, μ _ikrepresent i training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) i input variable X of expression _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.By (6) (7) (8) formula, can derive fuzzy group k is output as at training sample i:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;K<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein, for the output of fuzzy group k at training sample i.K<> is the kernel function of Weighted Least Squares Support Vector Machines, and K<> gets linear kernel function here; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) m input variable X of expression _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in Anti-fuzzy method obtains the output of last fuzzifying equation system:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

Wherein,

for the output of fuzzy group k at training sample i.

4), adopt particle cluster algorithm to be optimized the penalty factor of Weighted Least Squares Support Vector Machines local equation in fuzzifying equation and error margin value, specific implementation step is as follows:

1. the Optimal Parameters of determining population is the penalty factor of Weighted Least Squares Support Vector Machines local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

2. set optimization aim function, be converted into fitness, each On Local Fuzzy equation is evaluated; By corresponding error function, calculate fitness function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzifying equation system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula,

the prediction output of fuzzifying equation system, O _itarget output for fuzzifying equation system;

3. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _prepresent the more speed of new particle p, r _prepresent the more position of new particle p, Lbest _prepresent the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter represents cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

4. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

If the 5. individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

6. judge whether to meet performance requirement, if so, finish optimizing, obtain the local equation parameter of one group of fuzzifying equation of optimizing; Otherwise return to step 3., continue iteration optimizing, until reach maximum iteration time iter _max.

Described flexible measurement method is further comprising the steps of: 5), regularly off-line analysis data is input in training set, upgrade fuzzifying equation model.

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