CN103675011B - The industrial melt index soft measurement instrument of optimum support vector machine and method - Google Patents

Abstract

The industrial melt index soft that the invention discloses a kind of optimum support vector machine measures soft measuring instrument and method.This flexible measurement method carries out Fuzzy processing to the output of multiple Weighted Least Squares Support Vector Machines, and adopts particle cluster algorithm to be optimized whole fuzzifying equation system, to obtain optimum hard measurement result.In the present invention, for measure easily survey variable field intelligent instrument, be connected with DCS database for the control station measuring performance variable, hard measurement value display instrument comprises the industrial melt index soft measurement model of optimum support vector machine, DCS database is connected with the input end of soft-sensing model, and the output terminal of the industrial melt index soft measurement model of described optimum support vector machine is connected with melt index flexible measured value display instrument; The present invention has on-line optimization parameter, model upgrades automatically, noise resisting ability strong, promote the good feature of performance.

Description

The industrial melt index soft measurement instrument of optimum support vector machine and method

Technical field

The present invention relates to soft measuring instrument and method, particularly relate to a kind of industrial melt index soft measurement instrument and method of optimum support vector machine.

Background technology

Polypropylene is a kind of hemicrystalline thermoplastics by propylene polymerization, and have higher resistance to impact, engineering properties is tough, and anti-multiple organic solvent and acid and alkali corrosion, being widely used in industry member, is one of usual modal macromolecular material.Melting index (MI) determines one of important quality index of the final products trade mark during polypropylene is produced, and which determines the different purposes of product.Melting index accurate, measure timely, to production and scientific research, have very important effect and directive significance.But the on-line analysis of melting index is measured and is still difficult to accomplish at present, the in-line analyzer lacking melting index is a subject matter of restriction polypropylene product quality.MI can only pass through hand sampling, off-line assay obtains, and general every 2-4 hour analyzes once, and time lag is large, is difficult to meet the requirement of producing and controlling in real time.

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

Nineteen sixty-five U.S. mathematician L.Zadeh first proposed the concept of fuzzy set.Subsequently fuzzy logic with its problem closer to daily people and the meaning of one's words statement mode, start the classical logic replacing adhering to that all things can represent with binary item.Fuzzy logic so far successful Application among multiple fields of industry, the such as field such as household electrical appliances, Industry Control.2003, Demirci proposed the concept of fuzzifying equation, by using fuzzy membership matrix and the input matrix new with its distortion structure one, then in local equation, showed that analytic value is as last output using the gravity model appoach in Anti-fuzzy method.For the hard measurement of melting index in propylene polymerization production process, consider the noise effect in industrial processes and operate miss, the fuzzy performance of fuzzy logic can be used to reduce error to the impact of whole forecast precision.

Support vector machine, was introduced in 1998 by Vapnik, due to the Generalization Ability that it is good, is widely used in pattern-recognition, matching and classification problem.Because standard support vector machine is to isolated point and noise sensitivity, so also been proposed Weighted Least Squares Support Vector Machines afterwards.Weighted Least Squares Support Vector Machines can process the sample data with noise better compared to standard support vector machine, is selected as the local equation in fuzzifying equation here.

Particle cluster algorithm, i.e. Particle Swarm Optimization, being a kind of a kind of biological intelligence optimizing algorithm seeking global optimum by imitating birds flight data dispose put forward by Kennedy and Eberhart professor, being called for short PSO.This algorithm is influenced each other by interparticle in colony, decreases the risk that searching algorithm is absorbed in 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 the measuring accuracy overcoming existing propylene polymerization production process is not high, low to noise sensitivity, promote the deficiency of poor performance, the invention provides a kind of on-line measurement, computing velocity be fast, model upgrades automatically, noise resisting ability is strong, promote industrial melt index soft measurement instrument and the method for the good optimum support vector machine of performance.

A kind of industrial melt index soft measurement instrument of optimum support vector machine, comprise propylene polymerization production process, for measuring the field intelligent instrument easily surveying 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 melt index soft measurement model of optimum support vector machine, described DCS database is connected with the input end of the industrial melt index soft measurement model of described optimum support vector machine, the output terminal of the industrial melt index soft measurement model of described optimum support vector machine is connected with melt index flexible measured value display instrument, the industrial melt index soft measurement model of described optimum support vector machine comprises:

Data preprocessing module, carries out pre-service for the model training sample will inputted from DCS database, to training sample centralization, namely deducts the mean value of sample, then carries out standardization to it:

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-th training sample, N is number of training, for the average of training sample, X is the training sample after standardization.σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample.

Fuzzifying equation module, to the training sample X passed from data preprocessing module after 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, then i-th training sample X after standardization _ifor the degree of membership μ of 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 needed in fuzzy classification process, is usually taken as 2, || || be norm expression formula.

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

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

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, generally get exp (μ _ik) etc., Φ _ik(X _i, μ _ik) represent i-th input variable X _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-th of model training sample target exports 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-th 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 is weight and the penalty factor of Weighted Least Squares Support Vector Machines respectively, i-th 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-th training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

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

Wherein, for fuzzy group k is in the output of 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) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

The output of last fuzzifying equation system is obtained by the gravity model appoach in Anti-fuzzy method:

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

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

Particle cluster algorithm optimizes module, and for adopting 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. determine that the Optimal Parameters of population is penalty factor and error margin value, population individual amount popsize, the largest loop optimizing number of times iter of Weighted Least Squares Support Vector Machines local equation _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 object function, be converted into fitness, each On Local Fuzzy equation is evaluated; Calculate fitness function by corresponding error 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 _ifor the target of fuzzifying equation system exports;

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

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 speed of more new particle p, r _prepresent the position of more new particle p, Lbest _prepresent the individual optimal value of more 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 more individual optimal value of new particle:

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, terminate optimizing, obtain the local equation parameter of the fuzzifying equation that a group is optimized; Otherwise return step 3., continue iteration optimizing, until reach maximum iteration time iter _max.

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

An industrial melt index soft measuring 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 the input of variable as model, performance variable and easily survey variable are obtained by DCS database;

2), by the model training sample inputted from DCS database carry out pre-service, to training sample centralization, namely deduct the mean value of sample, then carry out standardization to it, make its average be 0, variance is 1.This process adopts following formula process:

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

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

Wherein, TX _ibe i-th training sample, N is number of training, for the average of training sample, X is the training sample after standardization.σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample.

3), to passing the training sample of coming from data preprocessing module, obfuscation is carried out.If have c in fuzzifying equation system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, then i-th training sample X after standardization _ifor the degree of membership μ of 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 needed in fuzzy classification process, is usually taken as 2, || || be norm expression formula.

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

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

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, generally get exp (μ _ik) etc., Φ _ik(X _i, μ _ik) represent i-th input variable X _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-th of model training sample target exports 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-th 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 is weight and the penalty factor of Weighted Least Squares Support Vector Machines respectively, i-th 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-th training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

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

Wherein, for fuzzy group k is in the output of 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) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

The output of last fuzzifying equation system is obtained by the gravity model appoach in Anti-fuzzy method:

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

Wherein, for fuzzy group k is in the output of 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. determine that the Optimal Parameters of population is penalty factor and error margin value, population individual amount popsize, the largest loop optimizing number of times iter of Weighted Least Squares Support Vector Machines local equation _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 object function, be converted into fitness, each On Local Fuzzy equation is evaluated; Calculate fitness function by corresponding error 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 _ifor the target of fuzzifying equation system exports;

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

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 speed of more new particle p, r _prepresent the position of more new particle p, Lbest _prepresent the individual optimal value of more 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 more individual optimal value of new particle:

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, terminate optimizing, obtain the local equation parameter of the fuzzifying equation that a group is optimized; Otherwise return 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), be regularly input in training set by off-line analysis data, upgrade fuzzifying equation model.

Technical conceive of the present invention is: carry out online soft sensor to the important quality index melting index of propylene polymerization production process, 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 and Automatic Optimal is carried out to fuzzifying equation model, 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 relative to existing melting index soft-sensing model: (1) reduces noise and manual operation error to the impact of model prediction precision; (2) enhance the popularization performance of model, over-fitting is effectively suppressed; (3) automatic optimal is carried out to the parameter of model, improve the stability of model, reduce 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 melt index soft measurement instrument of optimum support vector machine and the basic structure schematic diagram of method;

Fig. 2 is the industrial melt index soft measurement 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 explaining and the present invention is described, instead of limits the invention, and in the protection domain of spirit of the present invention and claim, any amendment make the present invention and change, all fall into protection scope of the present invention.

Embodiment 1

With reference to Fig. 1, Fig. 2, a kind of industrial melt index soft measurement instrument of optimum support vector machine, comprise propylene polymerization production process 1, for measuring the field intelligent instrument 2 easily surveying 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 that particle cluster algorithm optimizes Weighted Least Squares Support Vector Machines fuzzifying equation, described DCS database 4 is connected with the input end of the industrial melt index soft measurement model 5 of described optimum support vector machine, the output terminal of the industrial melt index soft measurement model 5 of described optimum support vector machine is connected with melt index flexible measured value display instrument 6, the industrial melt index soft measurement model of described optimum support vector machine comprises:

Data preprocessing module, carries out pre-service for the model training sample will inputted from DCS database, to training sample centralization, namely deducts the mean value of sample, then carries out standardization to it:

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-th training sample, N is number of training, for the average of training sample, X is the training sample after standardization.σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample.

Fuzzifying equation module, to the training sample X passed from data preprocessing module after 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, then i-th training sample X after standardization _ifor the degree of membership μ of 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 needed in fuzzy classification process, is usually taken as 2, || || be norm expression formula.

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

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

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, generally get exp (μ _ik) etc., Φ _ik(X _i, μ _ik) represent i-th input variable X _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-th of model training sample target exports 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-th 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 is weight and the penalty factor of Weighted Least Squares Support Vector Machines respectively, i-th 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-th training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

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

Wherein, for fuzzy group k is in the output of 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) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

The output of last fuzzifying equation system is obtained by the gravity model appoach in Anti-fuzzy method:

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

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

Particle cluster algorithm optimizes module, and for adopting 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. determine that the Optimal Parameters of population is penalty factor and error margin value, population individual amount popsize, the largest loop optimizing number of times iter of Weighted Least Squares Support Vector Machines local equation _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 object function, be converted into fitness, each On Local Fuzzy equation is evaluated; Calculate fitness function by corresponding error 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 _ifor the target of fuzzifying equation system exports;

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

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 speed of more new particle p, r _prepresent the position of more new particle p, Lbest _prepresent the individual optimal value of more 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 more individual optimal value of new particle:

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, terminate optimizing, obtain the local equation parameter of the fuzzifying equation that a group is optimized; Otherwise return step 3., continue iteration optimizing, until reach maximum iteration time iter _max.

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

According to reaction mechanism and flow process analysis, consider in polypropylene production process the various factors that melting index has an impact, 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 lists 9 the modeling variablees inputted as soft-sensing model 5, to be respectively in temperature in the kettle (T), still in pressure (p), still hydrogen volume concentration (X in liquid level (L), 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, and therefore mode input variable relates to the mean value in process variable employing front some moment of material.The data acquisition mean value of last hour in this example.Melting index off-line laboratory values is as the output variable of soft-sensing model 5.Obtained by hand sampling, off-line assay, 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 hard measurement value display instrument 6.Field intelligent instrument 2 measures the easy survey variable that propylene polymerization produces object, is transferred to DCS database 4 by easily surveying variable; Control station 3 controls the performance variable that propylene polymerization produces object, performance variable is transferred to DCS database 4.In DCS database 4, the variable data of record is as the input of the industrial melt index soft measurement model 5 of optimum support vector machine, hard measurement value display instrument 6 for showing the output of the industrial melt index soft measurement model 5 of optimum support vector machine, i.e. hard measurement value.

Table 1: modeling variable needed for the industrial melt index soft measurement 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

L	Liquid level in still	f3	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 melt index soft measurement model 5 of optimum support vector machine, comprises following 4 parts:

Data preprocessing module 7, carries out pre-service for the model training sample will inputted from DCS database, to training sample centralization, namely deducts the mean value of sample, then carries out standardization to it:

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-th training sample, N is number of training, for the average of training sample, X is the training sample after standardization.σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample.

Fuzzifying equation module 8, to the training sample X passed from data preprocessing module after 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, then i-th training sample X after standardization _ifor the degree of membership μ of 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 needed in fuzzy classification process, is usually taken as 2, || || be norm expression formula.

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

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

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, generally get exp (μ _ik) etc., Φ _ik(X _i, μ _ik) represent i-th input variable X _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-th of model training sample target exports 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-th 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 is weight and the penalty factor of Weighted Least Squares Support Vector Machines respectively, i-th 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-th training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

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

Wherein, for fuzzy group k is in the output of 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) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

The output of last fuzzifying equation system is obtained by the gravity model appoach in Anti-fuzzy method:

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

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

Particle cluster algorithm optimizes module 9, and for adopting 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. determine that the Optimal Parameters of population is penalty factor and error margin value, population individual amount popsize, the largest loop optimizing number of times iter of Weighted Least Squares Support Vector Machines local equation _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 object function, be converted into fitness, each On Local Fuzzy equation is evaluated; Calculate fitness function by corresponding error 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 _ifor the target of fuzzifying equation system exports;

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

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 speed of more new particle p, r _prepresent the position of more new particle p, Lbest _prepresent the individual optimal value of more 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 more individual optimal value of new particle:

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, terminate optimizing, obtain the local equation parameter of the fuzzifying equation that a group is optimized; Otherwise return 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 in training set by off-line analysis data, upgrades fuzzifying equation model.

Embodiment 2

With reference to Fig. 1, Fig. 2, a kind of industrial polypropylene producing melt index flexible measurement method 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 the input of variable as model, performance variable and easily survey variable are obtained by DCS database;

2), by the model training sample inputted from DCS database carry out pre-service, to training sample centralization, namely deduct the mean value of sample, then carry out standardization to it, make its average be 0, variance is 1.This process adopts following formula process:

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

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

Wherein, TX _ibe i-th training sample, N is number of training, for the average of training sample, X is the training sample after standardization.σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample.

3), to passing the training sample of coming from data preprocessing module, obfuscation is carried out.If have c in fuzzifying equation system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, then i-th training sample X after standardization _ifor the degree of membership μ of 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 needed in fuzzy classification process, is usually taken as 2, || || be norm expression formula.

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

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

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, generally get exp (μ _ik) etc., Φ _ik(X _i, μ _ik) represent i-th input variable X _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-th of model training sample target exports 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-th 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 is weight and the penalty factor of Weighted Least Squares Support Vector Machines respectively, i-th 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-th training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

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

Wherein, for fuzzy group k is in the output of 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) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

The output of last fuzzifying equation system is obtained by the gravity model appoach in Anti-fuzzy method:

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

Wherein, for fuzzy group k is in the output of 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. determine that the Optimal Parameters of population is penalty factor and error margin value, population individual amount popsize, the largest loop optimizing number of times iter of Weighted Least Squares Support Vector Machines local equation _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 object function, be converted into fitness, each On Local Fuzzy equation is evaluated; Calculate fitness function by corresponding error 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 _ifor the target of fuzzifying equation system exports;

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

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 speed of more new particle p, r _prepresent the position of more new particle p, Lbest _prepresent the individual optimal value of more 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 more individual optimal value of new particle:

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, terminate optimizing, obtain the local equation parameter of the fuzzifying equation that a group is optimized; Otherwise return 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), be regularly input in training set by off-line analysis data, 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, selects performance variable and easily surveys the input of variable as model.Performance variable and easily survey variable are obtained by DCS database 4.

Step 2: carry out pre-service to sample data, is 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, export data and chemically examine acquisition by off-line.

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

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

Step 6: melt index flexible measured value display instrument 6 shows the output of the industrial melt index soft measurement model 5 of optimum support vector machine, completes the display of measuring industrial polypropylene producing melt index flexible.

Claims (2)

1. the industrial melt index soft measurement instrument of an optimum support vector machine, comprise for measuring the field intelligent instrument easily surveying 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 melt index soft measurement model of optimum support vector machine, described DCS database is connected with the input end of the industrial melt index soft measurement model of described optimum support vector machine, the output terminal of the industrial melt index soft measurement model of described optimum support vector machine is connected with melt index flexible measured value display instrument, the industrial melt index soft measurement model of described optimum support vector machine comprises:

Data preprocessing module, carries out pre-service for the model training sample will inputted from DCS database, to training sample centralization, namely deducts the mean value of sample, then carries out standardization to it:

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-th training sample, N is number of training, for the average of training sample, X is the training sample after standardization; σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample;

Fuzzifying equation module, to the training sample X passed from data preprocessing module after 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, then i-th training sample X after standardization _ifor the degree of membership μ of 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 needed in fuzzy classification process, is taken as 2, || || be norm expression formula;

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

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

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, get exp (μ _ik), Φ _ik(X _i, μ _ik) represent i-th input variable X _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-th of model training sample target exports 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-th 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 is weight and the penalty factor of Weighted Least Squares Support Vector Machines respectively, i-th 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-th training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix; Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

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

Wherein, for fuzzy group k is in the output of 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) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix;

The output of last fuzzifying equation system is obtained by the gravity model appoach in Anti-fuzzy method:

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

Wherein, for fuzzy group k is in the output of training sample i;

Particle cluster algorithm optimizes module, and for adopting 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. determine that the Optimal Parameters of population is penalty factor and error margin value, population individual amount popsize, the largest loop optimizing number of times iter of Weighted Least Squares Support Vector Machines local equation _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 object function, be converted into fitness, each On Local Fuzzy equation is evaluated; Calculate fitness function by corresponding error 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 _ifor the target of fuzzifying equation system exports;

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

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 speed of more new particle p, r _prepresent the position of more new particle p, Lbest _prepresent the individual optimal value of more 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 more individual optimal value of new particle:

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, terminate optimizing, obtain the local equation parameter of the fuzzifying equation that a group is optimized; Otherwise return step 3., continue iteration optimizing, until reach maximum iteration time iter _max;

The industrial melt index soft measurement model of described optimum support vector machine also comprises:

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

2., with the flexible measurement method that the industrial melt index soft measurement instrument of optimum support vector machine as claimed in claim 1 realizes, it 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 the input of variable as model, performance variable and easily survey variable are obtained by DCS database;

2), by the model training sample inputted from DCS database carry out pre-service, to training sample centralization, namely deduct the mean value of sample, then carry out standardization to it, make its average be 0, variance is 1; This process adopts following formula process:

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

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

Wherein, TX _ibe i-th training sample, N is number of training, for the average of training sample, X is the training sample after standardization; σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample;

3), to passing the training sample of coming from data preprocessing module, obfuscation is carried out; If have c in fuzzifying equation system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, then i-th training sample X after standardization _ifor the degree of membership μ of 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 needed in fuzzy classification process, is taken as 2, || || be norm expression formula;

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

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

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, get exp (μ _ik), Φ _ik(X _i, μ _ik) represent i-th input variable X _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-th of model training sample target exports 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-th 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 is weight and the penalty factor of Weighted Least Squares Support Vector Machines respectively, i-th 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-th training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix; Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

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

Wherein, for fuzzy group k is in the output of 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) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix;

The output of last fuzzifying equation system is obtained by the gravity model appoach in Anti-fuzzy method:

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

Wherein, for fuzzy group k is in the output of 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. determine that the Optimal Parameters of population is penalty factor and error margin value, population individual amount popsize, the largest loop optimizing number of times iter of Weighted Least Squares Support Vector Machines local equation _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 object function, be converted into fitness, each On Local Fuzzy equation is evaluated; Calculate fitness function by corresponding error 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 _ifor the target of fuzzifying equation system exports;

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

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 speed of more new particle p, r _prepresent the position of more new particle p, Lbest _prepresent the individual optimal value of more 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 more individual optimal value of new particle:

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, terminate optimizing, obtain the local equation parameter of the fuzzifying equation that a group is optimized; Otherwise return step 3., continue iteration optimizing, until reach maximum iteration time iter _max;

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