CN107782857A - Flexible measurement method based on the edible fungus fermented process key parameter for improving CS BPNN - Google Patents

Flexible measurement method based on the edible fungus fermented process key parameter for improving CS BPNN Download PDF

Info

Publication number
CN107782857A
CN107782857A CN201710928140.9A CN201710928140A CN107782857A CN 107782857 A CN107782857 A CN 107782857A CN 201710928140 A CN201710928140 A CN 201710928140A CN 107782857 A CN107782857 A CN 107782857A
Authority
CN
China
Prior art keywords
bird
variable
value
particle
edible fungus
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201710928140.9A
Other languages
Chinese (zh)
Inventor
朱湘临
宋彦
丁煜函
王博
郝建华
华天争
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangsu University
Original Assignee
Jiangsu University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiangsu University filed Critical Jiangsu University
Priority to CN201710928140.9A priority Critical patent/CN107782857A/en
Publication of CN107782857A publication Critical patent/CN107782857A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02—Food
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • General Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • Food Science & Technology (AREA)
  • Chemical & Material Sciences (AREA)
  • Medicinal Chemistry (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

The invention discloses the flexible measurement method based on the edible fungus fermented process key parameter for improving CS BPNN, this method is analyzed edible fungus fermented process mechanism first, is chosen suitable external variable and is used based on discrete PSO choosing auxiliary variables algorithm to choose auxiliary variable of the most suitable external variable as soft-sensing model；Training sample database is established according to history tank batch data, it is normalized；Then a BP neural network model is designed, utilizes the weights and threshold value of improved cuckoo algorithm optimization neutral net；Neutral net after optimization is trained, hypha biomass is predicted with the model optimized.The present invention is using improved cuckoo algorithm come Optimized BP Neural Network, compared with the CS BP neural network methods of standard, convergence rate and local search ability greatly promote, the real-time online for realizing edible fungus fermented process key variable is accurately predicted, significant to the parameter detecting and optimal control of edible fungus fermented process.

Description

Flexible measurement method based on the edible fungus fermented process key parameter for improving CS-BPNN
Technical field
The present invention be it is a kind of be used to solving it is edible fungus fermented during be difficult to bacterium with physical sensors On-line sampling system The On-line Estimation problem of this critical biochemical variable of silk biomass, belong to the technical field of hard measurement and soft instrument construction.
Background technology
Fermentation is a kind of important industrial production engineering, and it is typically characterised by, and inherent mechanism is complicated, repeatability is poor, production Fluctuation is poor, has the non-linear and time-varying characteristics of height；On-line measurement is carried out to it, is to carry out the dynamic controls such as feed supplement, oxygen supply Important evidence, while be also to optimize premise and the basis of scheduling.In general, measuring for significant process variable is to pass through Laboratory periodically samples, off-line operation, analysis measure.There is large effect in the cycle of sampling to the variable value measured, And often lagged on the time, cause variable information to lag so that fermentation process is difficult to real-time closed-loop optimal control.Therefore, Research how to obtain in time it is edible fungus fermented during key variables status information, to fermentation process build optimal growth ring Border, and then implement optimal control, improve product yield and be respectively provided with significance with quality.
BP neural network hard measurement is a kind of data-driven flexible measurement method widely used in recent years, because its is excellent None-linear approximation ability, the soft sensor modeling of nonlinear system process it has been widely used to.In the present invention, for food With the problems such as bacterium fermentation process is non-linear, large time delay, easy microbiological contamination and critical biochemical parameter are difficult to real-time online measuring, one is proposed Kind improves the soft-measuring modeling method of CS-BP neutral nets.This method enables CS algorithms to ensure global optimizing early stage in search Power；In the search later stage, local message is made full use of, search precision is improved, shortens learning time.
The content of the invention
In order to solve it is edible fungus fermented during it is extremely important, but be difficult to physical sensors On-line sampling system or in real time The deficiency of the measuring method of the very high quantity of state of cost is measured, the present invention proposes a kind of based on the edible mushroom for improving CS-BPNN Fermentation process key variables flexible measurement method, the measurement signal of the input variable provided by conventional on-line measurement instrument, is provided The predicted value of current key state variable, parameter detecting and optimization operation for edible fungus fermented process provide critical process and referred to Mark.
Technical scheme is as follows：
Based on the edible fungus fermented process key state variable flexible measurement methods of improved CS-BPNN, comprise the following steps：
Step 1：Choosing auxiliary variables, choose can direct measurement and the external variable closely related with process, with based on from The choosing auxiliary variables algorithm for dissipating PSO chooses auxiliary variable of the most suitable external variable as soft-sensing model；
Step 2：Tranining database is established, gathers the auxiliary variable and key state of some history tank batches under same process Variable data, the set of construction input and output vector pair, generates muscle-setting exercise sample database；And it is normalized place Reason；Wherein input vector is auxiliary variable, and output vector is key stato variable；
Step 3：BP neural network model is designed, the neuron number of its input, hidden layer and output layer is set；
Step 4：Using the weights and threshold value of improved cuckoo algorithm optimization BP neural network, it is best to find prediction effect Weights and threshold value；Neutral net after training optimization obtains soft-sensing model.
Step 5：Using the soft-sensing model trained, vector is newly entered according to current tank to be predicted batch, obtained Obtain the predicted value of key stato variable.
Further, step 1 includes：
Step 1.1, fermentation process external variable data are gathered：Pass through gas flow sensor, speed probe, CO2Gas Quick electrode, liquid-level probe, thermal resistance, dissolved oxygen electrode, pH electrodes, peristaltic pump collection air mass flow q, motor speed of agitator r, CO2Burst size u, fermentating liquid volume v, fermentation jar temperature t, dissolved oxygen DO, zymotic fluid acidity-basicity ph, glucose feeding speed ρ, Ammonia aqua stream rate of acceleration η；Offline inspection obtains key stato variable, including mycelia biology after zymotic fluid is centrifuged device separation Measure S；
Step 1.2, the choosing auxiliary variables algorithm based on discrete PSO, finds optimal location and the particle of speed, by its from Dispersion, obtain most suitable auxiliary variable combination.Specific implementation includes：
The detailed process of the step 1.2 includes：
Step 1.2.1：Initialize discrete PSO population and parameter, initialization population number M=22, inertia weight w=0.8, Aceleration pulse c1=c2=0.7, maximum speed limit Vmax=4；Randomly generate M particle x of element 0 and 1i=(xi1,xi2,..., xiD), i=1 ..., M, form initial population；Randomly generate initial velocity v of the random number between (0,1) as each particleij, Form initial velocity matrix VM×D.The history desired positions p of each particleiWith population desired positions pgInitial value is all set to D dimensions 0 Vector；
Step 1.2.2：Evaluate the adaptive value of each particle；According to PLS regression algorithms and formula The fitness function of each particle is calculated, seeks its adaptive value；Wherein, yi,actFor sample i actual measured value, y-i,predFor with The model of the data foundation of i-th of sample is removed to yi,actPredicted value, yavgFor yi,actAverage value, h for obtain maximum When pivot number,Predictive ability closer to 1 explanation model is better；
Step 1.2.3：More new particle optimal location.To each particle xi, by its adaptive value f (xi) lived through most with it Good position piCorresponding adaptive value f (pi) make comparisons, if f (xi) it is better than f (pi), then by xiReplace with current desired positions；
Step 1.2.4：Population Regeneration optimal location.To desired positions p in current population (M particle)best, corresponded to Adaptive value f (pbest) the desired positions p that is lived through with populationgCorresponding adaptive value f (pg) make comparisons, if f (pbest) excellent In f (pg), then by pbestReplace with population optimal location；To prevent result to be absorbed in local optimum, by population optimal location pgNote After lower, then that particle corresponding to it reinitialized；
Step 1.2.5：At the t+1 moment, particle i is in d (d=1, the 2 ..., D) speed of dimension space and the expression of position Formula is： vid(t+1)=w × vid(t)+c1r1×(pid(t)-xid(t))+c2r2×(pgd(t)-xid) and x (t)id(t+1)=xid (t)+vid(t+1)；Position and the speed of current particle are adjusted according to the two formula, and according to formula By the particle discretization after renewal；
Step 1.2.6：Judge whether to reach end condition, if above-mentioned condition meets, terminate iteration, otherwise return to step 1.2.2。
Further, step 3 realize it is specific as follows：
The network structure of selection is three-layer neural network structure, determines that input layer is arranged to 5 according to the number of auxiliary variable Individual neuron, determine that output layer is arranged to 1 neuron according to the number of key stato variable, according to structured approach empirical equation Midn=2inn+1 sets the nodes of hidden layer, and wherein midn is node in hidden layer, and inn is input layer number.
Further, the realization of step 4 comprises the following steps：
Step 4.1, BP neural network is regarded to the fitness function for improving CS algorithms as, then using algorithm itself The strong advantage of global optimizing ability goes to seek optimal weight threshold combination.BP network structures are primarily determined that according to sample dimension, Weights and threshold total number are determined successively, and then determine to improve the code length of cuckoo individual in CS algorithms.Randomly generate n Bird's Nest xi(i=1,2 ..., n), each Bird's Nest represents the weights and threshold value of one group of neutral net that will optimize training, sets Population scale n, maximum iteration Nmax, maximum probability of detection pamax, minimum probability of detection pamin, probability of detection pa；
Step 4.2, according to fitness functionCurrent optimal Bird's Nest is selected in calculatingWherein n is Total sample number, y'(i) i-th sample hands-on output valve, y (i) is the desired output of i-th of sample.WillProtect The next generation is left to, other Bird's Nests are according to formulaCarry out location updating, wherein i=1,2 ..., n；Represent position of i-th of Bird's Nest in the t times iteration, a is step-length scale factor, n be Bird's Nest quantity,Represent dot product；L (λ) obeys L é vy distributions, is random flight step-length.I.e.：Wherein,For t The optimum position of generation storage, μ and the equal Normal Distributions of ξ.The fitness value of these Bird's Nest positions is calculated simultaneously, is done with the previous generation Compare one group chosen and remain into the next generation, obtain one group of preferably Bird's Nest position；
Step 4.3, according to formulaTo paEnter Row renewal, wherein pamaxAnd paminThe maximum and minimum value of probability of detection is represented respectively；NmaxFor maximum iteration, NiTo work as Preceding iterations (1≤Ni≤Nmax).Produce and obey equally distributed random number r ∈ (0,1), with paContrast, if r ＞ pa, then it is right Bird's nest position is changed at random, on the contrary then constant, i.e., changes the larger bird's nest position of probability of detection at random, retain probability of detection Less bird's nest position；
Step 4.4, the Bird's Nest position after re-test changes, and contrasted with previous generation Bird's Nests.It is finally more excellent in test result One group of Bird's NestIn select the present age global optimum positionAnd judge its fminWhether essence is reached Degree requires.If meeting the requirements,For global optimum, return to step 4.2 if not meeting；
Step 4.5, the optimal solution vector of acquisition is subjected to Gray code operation, extracts the weights and threshold value of BP neural network Establish afterwards and improve CS-BPNN models, neutral net is trained using sample data, when training error no longer reduces, training then may be used Terminate.
Beneficial effects of the present invention：
1. the online process data that the present invention is provided using computer system and conventional instrumentation, only by a small amount of Artificial sample, the hard measurement for realizing the improvement CS-BP neutral nets of the key stato variable of edible fungus fermented process builds Mould, solves the problem for not having state variable on-line checking instrument to be difficult to on-line checking in fermentation process.
2. compared with manual sampling is chemically examined, reduce the workload of site operation personnel, reduce artificial in fermentation process The uncertainty of the measurement introduced is operated, improves the ageing of measurement, reduces asking of sampling that the data brought lag offline Topic.
3. the present invention using improved cuckoo algorithm come Optimized BP Neural Network, the CS-BP neutral net sides with standard Method is compared, and being overcome in the present invention in flexible measurement method in standard CS-BP neutral nets convergence rate and local search ability has The problem of be short of.
4. the present invention considers the factor for influenceing edible fungus fermented process key state variable comprehensively, largely using existing Conventional detection signal realizes the on-line prediction of key stato variable, using it is simple, easy, cost is relatively low, hard measurement result compared with Accurately.This method contributes to the optimal control of edible fungus fermented process and optimization to run.
Brief description of the drawings
Flow, measuring instrumentss and the allocation of computer figure of the edible fungus fermented processes of Fig. 1；
Fig. 2 is based on the edible fungus fermented key variables soft-sensing model Establishing process figure for improving CS-BP neutral nets；
In Fig. 1:1 biological fermentation tank, 2 steam generators, 3 air compressors, 4 air cleaners, 5 gas flows sensing Device, 6 speed probes, 7CO2Gas sensing electrode, 8 liquid-level probes, 9 thermal resistances, 10 dissolved oxygen electrodes, 11pH electrodes, 12 peristaltic pumps, 13 peristaltic pumps, 14 whizzers, 15 intelligent controllers, 16 host computers.
Label symbol used is as follows in Fig. 1：
Fermentation jar temperature-t, motor speed of agitator-r, fermentating liquid volume-v
Air mass flow-q, CO2Release rate-u, glucose feeding speed-ρ,
Ammonia aqua stream rate of acceleration-η, dissolved oxygen DO, zymotic fluid acid-base value-pH.
In Fig. 1, solid arrow represents logistics (zymotic fluid, water, air and steam) direction, and dotted line represents signal stream.
Embodiment
Below in conjunction with the implementing procedure shown in the examples of implementation and Fig. 2 of edible fungus fermented process key state variable prediction Figure, is described in detail to of the embodiment of the present invention.
Select auxiliary vector：Choosing auxiliary variables, energy direct measurement and the external variable closely related with process are chosen, used Auxiliary variable of the most suitable external variable as soft-sensing model is chosen based on discrete PSO choosing auxiliary variables algorithm；
1. the acquisition of fermentation process data
It is edible fungus fermented that fermented and cultured is carried out using SF-500L types fermentation monitoring system.Monitoring system as shown in Figure 1 is by sending out Fermentation tank 1, steam generator 2, air compressor 3, air cleaner and 4 water systems composition.Pass through during edible fungus fermented Gas flow sensor 5, speed probe 6, CO2 gas sensing electrodes 7, liquid-level probe 8, thermal resistance 9, dissolved oxygen electrode 10, pH electricity Pole 11, peristaltic pump 12 and peristaltic pump 13 gather air mass flow q, motor speed of agitator r, CO2Release rate u, fermentating liquid volume v, hair Fermentation tank temperature t, dissolved oxygen DO, zymotic fluid acidity-basicity ph, glucose feeding speed ρ and ammonia aqua stream rate of acceleration η.Zymotic fluid pass through from Offline inspection obtains hypha biomass after centrifugal separator 14 separates.
The process of choosing auxiliary variables based on discrete PSO is as follows：
For obtained external variable data (fermentation jar temperature t, motor speed of agitator r, fermentating liquid volume v, air mass flow q、 CO2Release rate u, glucose feeding speed ρ, ammonia aqua stream rate of acceleration η, dissolved oxygen DO, zymotic fluid acidity-basicity ph) be based on from The choosing auxiliary variables algorithm for dissipating PSO searches out auxiliary variable of the most suitable external variable as soft-sensing model.
Because hard measurement choosing auxiliary variables are the optimum organization problems of each independent variable, i.e., each particle is 0,1 composition A vector, 0, which represents certain variable, is not selected, 1 represent it is selected.This just needs to expand continuous PSO to binary space Exhibition, construct a kind of binary system particle Optimized model of discrete form.
If candidate's auxiliary variable collection is combined into m × D dimension spaces.The then position x of each particleiIt is D's to be expressed as one group of long degree 0th, 1 coded combination, it represents a kind of selection situation of auxiliary variable.The initial velocity v of particleiCan take between (0,1) with Machine number.Its iterative process is continuous, is no longer 0,1 binary number after each renewal of particle position.Therefore, in order to maintain grain The binary form of son, needed after particle position renewal by xiChange into 0 or 1.
It is comprised the following steps that：
Step 1：Discrete PSO population and parameter is initialized, initialization population number M=22, inertia weight w=0.8, is added Fast constant c1=c2=0.7, maximum speed limit Vmax=4.Randomly generate M particle x of element 0 and 1i=(xi1,xi2,..., xiD), i=1 ..., M, form initial population.Randomly generate initial velocity v of the random number between (0,1) as each particleij, Form initial velocity matrix VM×D.The history desired positions p of each particleiWith population desired positions pgInitial value is all set to D dimensions 0 Vector；
Step 2：Evaluate the adaptive value of each particle.According to PLS regression algorithms and formula The fitness function of each particle is calculated, seeks its adaptive value.Wherein, yi,actFor sample i actual measured value, y-i,predFor with The model of the data foundation of i-th of sample is removed to yi,actPredicted value, yavgFor yi,actAverage value, h for obtain maximum When pivot number,Predictive ability closer to 1 explanation model is better；
Step 3：More new particle optimal location.To each particle xi, by its adaptive value f (xi) the best position that is lived through with it Put pi=(pi1,pi2,…,pid) corresponding to adaptive value f (pi) make comparisons, if f (xi) it is better than f (pi), then by xiReplace with and work as Preceding desired positions；
Step 4：Population Regeneration optimal location.To desired positions p in current population (M particle)best, by corresponding to it Adaptive value f (pbest) the desired positions p that is lived through with populationg=(pg1,pg2,…,pgd) corresponding to adaptive value f (pg) make ratio Compared with if f (pbest) it is better than f (pg), then by pbestReplace with population optimal location., will to prevent result to be absorbed in local optimum Population optimal location pgAfter writing down, then that particle corresponding to it reinitialized；
Step 5：In the speed of d (d=1,2 ..., D) dimension space and position it is formula in t+1 moment particle i： vid(t+ 1)=w × vid(t)+c1r1×(pid(t)-xid(t))+c2r2×(pgd(t)-xid) and x (t)id(t+1)=xid(t)+vid(t+ 1).Wherein, r1,r2Random number respectively between (0,1),；Position and the speed of current particle are adjusted according to this two formula, and According to formulaBy the particle discretization after renewal；
Step 6：Judge whether to reach end condition, if above-mentioned condition meets, terminate iteration, otherwise return to step two.
According to above step from air mass flow q, broth temperature t, fermentating liquid volume v, CO2Release rate u, ammonia aqua stream accelerate CO is obtained in 9 variables such as rate η, glucose feeding speed ρ, dissolved oxygen DO, motor mixing speed r, zymotic fluid acidity-basicity ph2 5 release rate u, air mass flow q, dissolved oxygen DO, fermentating liquid volume v, zymotic fluid acidity-basicity ph variables are to hypha biomass shadow Ring maximum.
2. establish training sample database：Gather the auxiliary variable and key state of some history tank batches under same process Variable data, the set of construction input and output vector pair, generates muscle-setting exercise sample database, and it is normalized place Reason；Wherein input vector is auxiliary variable, and output vector is key stato variable.It is implemented as follows：
Edible fungus fermented process forms sample according to following structure, and collects some history tank batch instructions under same process Practice sample data, the data of 10 batches are collected in the embodiment of the present invention, wherein 90% data are made as sample data set, 10% For validation data set.Sample is expressed as { xk,yk, wherein xkFor the input of sample, that is, the auxiliary vector-air mass flow chosen Q, fermentating liquid volume v, CO2Release rate u, dissolved oxygen DO, zymotic fluid acidity-basicity ph.The output y of samplekDominated for be predicted Variable-hypha biomass S.
Training sample acquisition and recording structure such as table 2, the time is the sampling period in fermentation process, offline to reduce leading variable Error and Assay, sample choice is carried out using result of laboratory test three times according to same sample leading variable, finally averaged：
The sample data structure of table 2
Should be representative in view of sample data, and coverage is wider as far as possible, should at least include fermentation Process normal range of operation, manually regulate and control fermentation pressure tank, fermentation jar temperature and motor speed of agitator, in production technology Change the operating point of fermentation process, sample examination after each operating condition changes system steadily in the range of permission as far as possible.
3. build BP neural network model：Setting input layer is 5 neurons, and output layer is 1 neuron, by BP It is 11 neurons that hidden layer is chosen in the adaptive number experiment of neutral net.Wherein BP neural network input layer is 5 neurons, Fermentating liquid volume v, air mass flow q, CO are corresponded to respectively2Release rate u, dissolved oxygen DO, zymotic fluid acidity-basicity ph, 1 god of output layer Through member, to the key parameters in requisition for prediction --- hypha biomass.
4. using the BP neural network of improved cuckoo algorithm optimization step 3, optimal weights and threshold value are found, is obtained To the soft-sensing model of optimization.Shown in comprising the following steps that：
(1) first according to the dimension of sample data, i.e. the number of auxiliary variable determines the structure of neutral net, including power The sum of value and threshold value, and then determine to improve the dimension of search space in CS algorithms；Secondly, Bird's Nest coordinate and nerve net are constructed Mapping relations between network weight threshold.Weights and threshold value to be determined can be considered as to improve in CS algorithms and need what is found Bird's nest coordinate, all Bird's Nest monomers can be all regarded as one of combination of weights and threshold value, then this also represents one accordingly The structure of kind BP neutral nets, and the quality of any one Bird's Nest is all determined by fitness function.According to neural network weight With threshold value feature, cuckoo individual UVR exposure uses real number coding method, and each individual is represented by a real number string, the real number string It is made up of the weights between input layer, hidden layer, output layer and threshold value, and each cuckoo individual may pass through Gray code Extract all weights and threshold value.The search target of algorithm, exactly find coordinate (the i.e. one group of power for most adapting to Bird's Nest Value and threshold value), the fitness function of selection is worth to minimum value；Finally, BP neural network is assigned the Bird's Nest coordinate components It is used for building the CSBP neutral nets after improving for use as its weights and threshold value；
(2) network structure chosen is to apply three-layer neural network structure the most typical, and wherein input layer sets 5 god Through member, output layer sets 1 neuron, according to structured approach empirical equation：Midn=2inn+1, wherein midn are hidden layer section Points, inn is input layer number.When with improved CS Algorithm for Training BP neural network, it is net to define Bird's Nest location components The weights and threshold value of network.Connection weight number is 5*11+11*1=66, threshold value 11+1=12, so Bird's Nest dimension is set to 66 + 12=78.Bird's Nest population quantity is n=50, the maximum probability p that bird egg is foundamax=0.9, minimum probability of detection pamin= 0.1, iterations is set to Nmax=2000；
(3) according to fitness functionOptimal Bird's Nest is selected in calculatingWherein n is total sample number, Y'(i) the hands-on output valve of i-th of sample, y (i) are the desired output of i-th of sample.
Other Bird's Nest positions are according to formulaCarry out location updating, wherein i=1,2 ..., n；Represent position of i-th of Bird's Nest in the t times iteration；A is step-length scale factor；N is Bird's Nest quantity；Represent dot product；L (λ) obeys L é vy distributions, is random flight step-length.I.e.：WhereinFor t generations The optimum position of storage, μ and the equal Normal Distributions of ξ, random numbers of the β between (0,2).Calculate these Bird's Nest positions simultaneously Fitness value, one group chosen is compared with the previous generation and remains into the next generation, obtains one group of preferable Bird's Nest position.
(4) according to formulaTo paCarry out more Newly, wherein pamaxAnd paminThe maximum and minimum value of probability of detection is represented respectively；NmaxFor maximum iteration, NiChanged to be current Generation number (1≤Ni≤Nmax).Produce and obey equally distributed random number r ∈ (0,1), with paContrast, if r ＞ pa, then to bird's nest Position is changed at random, on the contrary then constant, i.e., changes the larger bird's nest position of probability of detection at random, retain probability of detection compared with Small bird's nest position.
(5) the Bird's Nest position after re-test changes, and contrasted with previous generation Bird's Nests.Finally in preferably one group of test result Bird's NestIn select the present age global optimum positionAnd judge its fminWhether required precision is reached. If meeting the requirements,For global optimum, (2) are returned if not meeting.
(6) optimal solution vector of acquisition is subjected to Gray code operation, extracts the weights and threshold value of all BP neural networks Establish afterwards and improve CS-BP models, neutral net is trained using sample data, when training error no longer reduces, training can then tie Beam.
5. predict key stato variable：The improved CS-BP neural network soft sensor models trained using step 4, root According to the predicted value for being newly entered vector, obtaining key stato variable of current tank to be predicted batch.It is implemented as follows：
After soft-sensing model is established, realized, and be embedded into intelligent controller 15 using embedded type C Programming with Pascal Language, As the input vector x of tank to be predicted batchi+1, after measuring instrumentss read in intelligent controller 15, intelligent controller 15 utilizes soft survey The predicted value of key stato variable is calculated in range sequence, and prediction result is sent into host computer 16 through data channel Upper real-time display.
Above-described embodiment is used for illustrating the present invention, rather than limits the invention, the present invention spirit and In scope of the claims, to any modifications and changes of the invention made, protection scope of the present invention is both fallen within.

Claims (9)

1. the flexible measurement method based on the edible fungus fermented process key parameter for improving CS-BPNN, it is characterised in that including as follows Step：
Step 1：Choosing auxiliary variables, energy direct measurement and the external variable closely related with process are chosen, with based on discrete PSO Choosing auxiliary variables algorithm choose auxiliary variable of the most suitable external variable as soft-sensing model；
Step 2：Tranining database is established, gathers the auxiliary variable and key stato variable of some history tank batches under same process Data, the set of construction input and output vector pair, generate muscle-setting exercise sample database；And it is normalized；Its Middle input vector is auxiliary variable, and output vector is key stato variable；
Step 3：BP neural network model is designed, the neuron number of its input, hidden layer and output layer is set；
Step 4：Using the weights and threshold value of improved cuckoo algorithm optimization BP neural network, the best power of prediction effect is found Value and threshold value, the soft-sensing model optimized；
Step 5：Using trained obtained optimization soft-sensing model, vector is newly entered according to current tank to be predicted batch, Obtain the predicted value of key stato variable.
2. the flexible measurement method according to claim 1 based on the edible fungus fermented process key parameter for improving CS-BPNN, Characterized in that, the detailed process of step 1 includes：
Step 1.1, fermentation process external variable data are gathered：Pass through gas flow sensor, speed probe, CO2Air-sensitive electricity Pole, liquid-level probe, thermal resistance, dissolved oxygen electrode, pH electrodes, peristaltic pump collection air mass flow q, motor speed of agitator r, CO2Release Amount u, fermentating liquid volume v, fermentation jar temperature t, dissolved oxygen DO, zymotic fluid acidity-basicity ph, glucose feeding speed ρ, ammonia aqua stream add Speed η；Offline inspection obtains key stato variable after zymotic fluid is centrifuged device separation, and the key stato variable includes bacterium Silk biomass S；
Step 1.2, the choosing auxiliary variables algorithm based on discrete PSO, optimal location and the particle of speed are found, its is discrete Change, obtain most suitable auxiliary variable combination.
3. the flexible measurement method according to claim 2 based on the edible fungus fermented process key parameter for improving CS-BPNN, Characterized in that, the detailed process of the step 1.2 includes：
Step 1.2.1：Discrete PSO population and parameter is initialized, initialization population number M=22, inertia weight w=0.8, is accelerated Constant c1=c2=0.7, maximum speed limit Vmax=4；Randomly generate M particle x of element 0 and 1i=(xi1,xi2,...,xiD), i =1 ..., M, form initial population；Randomly generate initial velocity v of the random number between (0,1) as each particleij, composition is initially Rate matrices VM×D.The history desired positions p of each particleiWith population desired positions pgInitial value is all set to the vector of D dimensions 0；
Step 1.2.2：Evaluate the adaptive value of each particle；According to PLS regression algorithms and formula The fitness function of each particle is calculated, seeks its adaptive value；Wherein, yi,actFor sample i actual measured value, y-i,predFor with removing Fall the model of the data foundation of i-th of sample to yi,actPredicted value, yavgFor yi,actAverage value, h for obtain maximum when Pivot number,Predictive ability closer to 1 explanation model is better；
Step 1.2.3：More new particle optimal location.To each particle xi, by its adaptive value f (xi) the best position that is lived through with it Put piCorresponding adaptive value f (pi) make comparisons, if f (xi) it is better than f (pi), then by xiReplace with current desired positions；
Step 1.2.4：Population Regeneration optimal location.To desired positions p in current population (M particle)best, will be suitable corresponding to it Should value f (pbest) the desired positions p that is lived through with populationgCorresponding adaptive value f (pg) make comparisons, if f (pbest) it is better than f (pg), then by pbestReplace with population optimal location；To prevent result to be absorbed in local optimum, by population optimal location pgAfter writing down, That particle corresponding to it is reinitialized again；
Step 1.2.5：At the t+1 moment, particle i is in d (d=1, the 2 ..., D) speed of dimension space and the expression formula of position： vid(t+1)=w × vid(t)+c1r1×(pid(t)-xid(t))+c2r2×(pgd(t)-xid) and x (t)id(t+1)=xid(t)+ vid(t+1)；Position and the speed of current particle are adjusted according to the two formula, and according to formulaWill Particle discretization after renewal；
Step 1.2.6：Judge whether to reach end condition, if above-mentioned condition meets, terminate iteration, otherwise return to step 1.2.2.
4. the flexible measurement method according to claim 1 based on the edible fungus fermented process key parameter for improving CS-BPNN, Characterized in that, the detailed process of step 3 includes：The Artificial Neural Network Structures of selection are three-layer neural network structure, input Layer number is set according to the number of auxiliary variable, and the number of output layer is set according to the number of key stato variable, hidden layer section Points are according to structured approach empirical equation：Midn=2inn+1 determines that wherein midn is node in hidden layer, and inn is input layer section Points.
5. the flexible measurement method according to claim 4 based on the edible fungus fermented process key parameter for improving CS-BPNN, Characterized in that, the input layer is arranged to 5 neurons, output layer is arranged to 1 neuron, and hidden layer is arranged to 11 god Through member.
6. the flexible measurement method according to claim 5 based on the edible fungus fermented process key parameter for improving CS-BPNN, Characterized in that, 5 neurons of the input layer correspond to fermentating liquid volume v, air mass flow q, CO respectively2Release rate u, dissolving Oxygen DO, zymotic fluid acidity-basicity ph；1 neuron of the output layer is to the key parameters in requisition for prediction.
7. the flexible measurement method according to claim 1 based on the edible fungus fermented process key parameter for improving CS-BPNN, Characterized in that, the detailed process of the step 4 includes：
Step 4.1, BP neural network is regarded as to the fitness function for improving CS algorithms, utilizes the global optimizing energy of algorithm itself The strong advantage of power goes to seek optimal weight threshold combination；BP network structures are primarily determined that according to sample dimension, determine power successively Value and threshold total number, and then determine to improve the code length of cuckoo individual in CS algorithms；
Randomly generate n Bird's Nest xi(i=1,2 ..., n), each Bird's Nest represents the power of one group of neutral net that will optimize training Value and threshold value, population scale n, maximum iteration N are setmax, maximum probability of detection pamax, minimum probability of detection pamin, find Probability pa；
Step 4.2, according to fitness functionCurrent optimal Bird's Nest is selected in calculatingWherein n is sample Sum, y'(i) i-th sample hands-on output valve, y (i) is the desired output of i-th of sample；WillRetain under A generation, other Bird's Nests are according to formulaCarry out location updating, wherein i=1,2 ..., n；Represent the Position of the i Bird's Nest in the t times iteration, a is step-length scale factor, n be Bird's Nest quantity,Represent dot product；L (λ) obeys L é Vy is distributed, and is random flight step-length；I.e.：Wherein,For the optimal of t generation storages Position, μ and the equal Normal Distributions of ξ；The fitness value of these Bird's Nest positions is calculated simultaneously, and one chosen is compared with the previous generation Group remains into the next generation, obtains one group of preferably Bird's Nest position；
Step 4.3, according to formulaTo paCarry out more Newly, wherein pamaxAnd paminThe maximum and minimum value of probability of detection is represented respectively；NmaxFor maximum iteration, NiFor current iteration Number (1≤Ni≤Nmax).Produce and obey equally distributed random number r ∈ (0,1), with paContrast, if r ＞ pa, then to bird's nest position Put and changed at random, it is on the contrary then constant, i.e., change the larger bird's nest position of probability of detection at random, it is less to retain probability of detection Bird's nest position；
Step 4.4, the Bird's Nest position after re-test changes, and contrasted with previous generation Bird's Nests.Finally in test result preferably one Group Bird's NestIn select the present age global optimum positionAnd judge its fminWhether reaching precision will Ask.If meeting the requirements,For global optimum, return to step 4.2 if not meeting；
Step 4.5, the optimal solution vector of acquisition is subjected to Gray code operation, extract BP neural network weights and threshold value after build It is vertical to improve CS-BPNN models, neutral net is trained using sample data, when training error no longer reduces, training can then terminate.
8. the flexible measurement method according to claim 1 based on the edible fungus fermented process key parameter for improving CS-BPNN, Characterized in that, the detailed process of step 5 includes：The soft-sensing model of optimization is realized using embedded type C Programming with Pascal Language, and it is embedding Enter in intelligent controller, as the input vector x of tank to be predicted batchi+1After measuring instrumentss read in intelligent controller, intelligent control The predicted value of key stato variable is calculated using hard measurement program for device.
9. the flexible measurement method according to claim 8 based on the edible fungus fermented process key parameter for improving CS-BPNN, Characterized in that, step 5 also includes：Prediction result is sent to real-time display on host computer through data channel.
CN201710928140.9A 2017-10-09 2017-10-09 Flexible measurement method based on the edible fungus fermented process key parameter for improving CS BPNN Pending CN107782857A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710928140.9A CN107782857A (en) 2017-10-09 2017-10-09 Flexible measurement method based on the edible fungus fermented process key parameter for improving CS BPNN

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710928140.9A CN107782857A (en) 2017-10-09 2017-10-09 Flexible measurement method based on the edible fungus fermented process key parameter for improving CS BPNN

Publications (1)

Publication Number Publication Date
CN107782857A true CN107782857A (en) 2018-03-09

Family

ID=61434203

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710928140.9A Pending CN107782857A (en) 2017-10-09 2017-10-09 Flexible measurement method based on the edible fungus fermented process key parameter for improving CS BPNN

Country Status (1)

Country Link
CN (1) CN107782857A (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109471363A (en) * 2018-12-28 2019-03-15 浙江大学 Industrial melt index soft measurement method based on aftereffect function and cuckoo search
CN109547431A (en) * 2018-11-19 2019-03-29 国网河南省电力公司信息通信公司 A kind of network security situation evaluating method based on CS and improved BP
CN110276483A (en) * 2019-06-04 2019-09-24 湖北大学 Sugaring raw material prediction technique neural network based
CN110309983A (en) * 2019-07-09 2019-10-08 重庆大学 A kind of industry cloud service scheduling of resource matching combined method
CN112307672A (en) * 2020-10-29 2021-02-02 上海电机学院 BP neural network short-term wind power prediction method based on cuckoo algorithm optimization
CN116013411A (en) * 2023-03-24 2023-04-25 北京蓝晶微生物科技有限公司 Quantitative relation model, application, fermentation monitoring method, device, system and equipment

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101748186A (en) * 2009-12-29 2010-06-23 江苏大学 FSVM-based lysine fermentation process key state variable soft measuring method and system
CN101929993A (en) * 2010-08-20 2010-12-29 江苏大学 Dynamic fuzzy neural network based penicillin fermentation process soft measuring modeling method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101748186A (en) * 2009-12-29 2010-06-23 江苏大学 FSVM-based lysine fermentation process key state variable soft measuring method and system
CN101929993A (en) * 2010-08-20 2010-12-29 江苏大学 Dynamic fuzzy neural network based penicillin fermentation process soft measuring modeling method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
孙晨 等: "基于布谷鸟算法优化BP神经网络模型的股价预测", 《计算机应用与软件》 *
李丽娟 等: "基于离散PSO的软测量辅助变量选择算法", 《系统仿真学报》 *
杨强大 等: "基于改进BP神经网络的菌体浓度软测量", 《控制与决策》 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109547431A (en) * 2018-11-19 2019-03-29 国网河南省电力公司信息通信公司 A kind of network security situation evaluating method based on CS and improved BP
CN109471363A (en) * 2018-12-28 2019-03-15 浙江大学 Industrial melt index soft measurement method based on aftereffect function and cuckoo search
CN109471363B (en) * 2018-12-28 2020-08-04 浙江大学 Industrial melt index soft measurement method based on post-effect function and rhododendron search
CN110276483A (en) * 2019-06-04 2019-09-24 湖北大学 Sugaring raw material prediction technique neural network based
CN110309983A (en) * 2019-07-09 2019-10-08 重庆大学 A kind of industry cloud service scheduling of resource matching combined method
CN112307672A (en) * 2020-10-29 2021-02-02 上海电机学院 BP neural network short-term wind power prediction method based on cuckoo algorithm optimization
CN116013411A (en) * 2023-03-24 2023-04-25 北京蓝晶微生物科技有限公司 Quantitative relation model, application, fermentation monitoring method, device, system and equipment
CN116013411B (en) * 2023-03-24 2023-05-30 北京蓝晶微生物科技有限公司 Quantitative relation model, application, fermentation monitoring method, device, system and equipment

Similar Documents

Publication Publication Date Title
CN107782857A (en) Flexible measurement method based on the edible fungus fermented process key parameter for improving CS BPNN
CN104123476B (en) Forecasting of Gas Concentration method based on extreme learning machine and its device
CN108090500B (en) Soft measurement method for key parameters in fermentation production process of edible fungi
CN111460728B (en) Method and device for predicting residual life of industrial equipment, storage medium and equipment
CN109407654B (en) Industrial data nonlinear causal analysis method based on sparse deep neural network
CN102231057B (en) Method for carrying out soft-sensing on lysine fermenting process on basis of chaos particle swarm optimization (CPSO)
CN106156401B (en) Multi-combination classifier based data driving system state model online identification method
CN101799888B (en) Industrial soft measurement method based on bionic intelligent ant colony algorithm
CN110023850A (en) Method and control device for control technology system
CN110738271B (en) Concentrate grade prediction method in zinc flotation process
CN112488235A (en) Elevator time sequence data abnormity diagnosis method based on deep learning
CN106052753A (en) Straw fermentation fuel ethanol production process key state variable soft measuring method based on fuzzy support vector machine
CN110807554A (en) Generation method and system based on wind power/photovoltaic classical scene set
CN110824915A (en) GA-DBN network-based intelligent monitoring method and system for wastewater treatment
CN108664009A (en) Divided stages based on correlation analysis and fault detection method
CN101748186A (en) FSVM-based lysine fermentation process key state variable soft measuring method and system
CN107341359A (en) Stalk fermentation produces the flexible measurement method of ethanol process key parameters
CN110046377B (en) Selective integration instant learning soft measurement modeling method based on heterogeneous similarity
CN101639902A (en) Modeling method of support vector machine (SVM)-based software measurement instrument in biological fermentation process
CN107766880B (en) Photosynthetic bacteria fermentation process key parameter soft measurement method based on BA-LSSVM
CN109408896B (en) Multi-element intelligent real-time monitoring method for anaerobic sewage treatment gas production
CN103279030B (en) Dynamic soft measuring modeling method and device based on Bayesian frame
CN114239397A (en) Soft measurement modeling method based on dynamic feature extraction and local weighted deep learning
CN105930936B (en) A kind of growth prediction method producing microorganism contained by food in the process of circulation
CN113988311B (en) Quality variable prediction method, device, terminal and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20180309