CN106951983A - Injector performance Forecasting Methodology based on the artificial neural network using many parent genetic algorithms - Google Patents

Injector performance Forecasting Methodology based on the artificial neural network using many parent genetic algorithms Download PDF

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CN106951983A
CN106951983A CN201710107962.0A CN201710107962A CN106951983A CN 106951983 A CN106951983 A CN 106951983A CN 201710107962 A CN201710107962 A CN 201710107962A CN 106951983 A CN106951983 A CN 106951983A
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蒋宁
潘凡
徐英杰
高增梁
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Zhejiang University of Technology ZJUT
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Abstract

A kind of injector performance Forecasting Methodology of the artificial neural network based on using many parent genetic algorithms, for given injector, collects relevant parameter；According to input/output argument, neutral net is set up；Genetic algorithm initial parameter is set；Set up evaluation function；Genetic algorithm processing, is constantly selected individual in population, many parents intersection, mutation operation and records fitness value, reach the evolution number of times upper limit；The corresponding chromosome of fitness optimal solution is the threshold value and weights corresponding to many parent BP GA neutral nets set up, and obtained many parent BP GA neutral nets are trained, until the error of prediction output variable is less than set-point；The input variable under virtual condition is measured, predicts that many parent BP GA neutral nets obtain Prediction Parameters by injector performance, by pcCarry out renormalization and obtain many parent BP GA neural network prediction outlet back pressures pcActual value.Precision of prediction of the present invention is higher, time-consuming shorter.

Description

Injector performance prediction based on the artificial neural network using many parent genetic algorithms Method
Technical field
The present invention relates to a kind of injector performance Forecasting Methodology, especially a kind of people based on using many parent genetic algorithms The injector performance Forecasting Methodology of artificial neural networks.
Background technology
When injector works, vacuum is produced by main jet by one pressure higher fluid, and suction pressure is relatively low Fluid, after mixing by diffuser improve fluid pressure, finally give the fluid of middle pressure, i.e., by low pressure fluid pressure Lifting, realize compression effect.Injector can be low-grade using industrial overbottom pressure, waste heat, used heat, solar heat, underground heat etc. The energy is as driving, without consumption electric power, with good effects of energy saving and emission reduction；Be widely used to chemical industry, heat energy, refrigeration, The fields such as HVAC.
Mass ratio of induced-to-inducing air (ε) and outlet back pressure (pc) under critical excitation are the performance parameters of injector most critical.But Due to injector interior flow it is extremely complex, including be jammed twice, Supersonic Flow, all kinds of shock waves, the phenomenon such as fan-shaped diffusion, adopt The parameters precision obtained with one-dimensional physical model simulation is relatively low, and effect is poor, such as (the W.Chen et of document 1 al.Theoretical analysis of ejector refrigeration system performance under overall modes.Applied energy,185-2:System is sprayed under the full working scopes such as 2074-2084,2016., i.e. W.Chen The theory analysis application energy of cooling system performance, 185-2:2074-2084,2016.) and (the JM.Cardemil et of document 2 al.A general model for evaluation of vapor ejectors performance for application in refrigeration.Energy Conversion and Management,64:79-86,2012, That is the conversion of model energy and management, 64 that the such as JM.Cardemil mono- are used for cooling steam jet ejector Performance Evaluation:79- 86,2012.) shown in, the mean error of conventional model is more in 5-10%, and worst error is up to more than 15%.As used The method of Fluid Mechanics Computation then takes long, also labor intensive material resources, is not suitable for design and the research of associated cyclic.It is above-mentioned existing Shape brings problem to work such as design application, the associated cyclic researchs of injector.
The content of the invention
In order to which the precision of prediction for overcoming the shortcomings of existing injector performance Forecasting Methodology is relatively low, time-consuming long, the present invention is carried The injector that a kind of precision of prediction is higher, take the shorter artificial neural network based on using many parent genetic algorithms is supplied Can Forecasting Methodology.
The technical solution adopted for the present invention to solve the technical problems is：
A kind of injector performance Forecasting Methodology of the artificial neural network based on using many parent genetic algorithms, including it is following Step：
Step one, the collection and processing of data
For given injector, it is citation jet body pressure Pe, working fluid pressure Pp, outlet back pressure to collect relevant parameter Pc and mass ratio of induced-to-inducing air ε, citation jet body pressure, working fluid pressure and outlet back pressure are normalized, and it is arrived [0,1] Between, formula is as follows：
Wherein, k is the data after normalization, and x is to be normalized data, xminTo be normalized the minimum value in data, xmaxTo be normalized maximum in data；
Step 2, the foundation of neural network structure
According to input/output argument, the input and output layer nodal point number of neutral net is determined, the hidden layer number of plies is one layer, node It is several according to empirical equation：Draw, wherein l, n are input, output node number, a takes 1~10 for constant.Judge The input layer number of neutral net, output layer nodes choose node in hidden layer, learning rate, training objective, cycle-index Set and each layer weights initial value；
Step 3, sets genetic algorithm initial parameter
Population scale, evolution number of times, crossover probability and mutation probability are set, the weights of BP neural network and threshold value are constituted The neutral net set up in genetic coding, the step 2 that reruns simultaneously records the genetic coding of each run completion and sets up population；
Step 4, sets up evaluation function, and the result that the neutral net constructed using every group chromosome is predicted is foundation, meter Calculate and record corresponding fitness value；
Step 5, genetic algorithm processing
A. selection operation：With certain probability selection individual for producing the next generation in old colony, it is general that individual is selected Rate is relevant with fitness value, and the value of fitness is better, and selected probability is bigger；
B. many parent crossover operations：B. many parent crossover operations：Refer to select a plurality of parent chromosome to carry out from population The foundation of chromosome of future generation, by the combined crosswise of chromosome, produces new individual；
C. mutation operation：Refer to an optional individual from colony, certain section coding in selected chromosome is become It is different to produce more excellent individual；
Step 6, the genetic coding in the chromosome newly obtained is decoded, and calculates fitness, and group enters with original seed Row compares, and selects the superior and eliminates the inferior；Four-step 5 of repeat step, constantly individual in population is selected, intersected, mutation operation simultaneously Fitness value is recorded, the evolution number of times upper limit is reached；The corresponding chromosome of fitness optimal solution is many parent BP-GA set up Obtained many parent BP-GA neutral nets are trained by threshold value and weights corresponding to neutral net, until prediction output becomes The error of amount is less than set-point；
Step 7, in practical engineering application, is carried out with many parent BP-GA neutral nets to certain given injector performance Input variable under prediction, measurement virtual condition, i.e. citation jet body pressure pe, working fluid pressure pp, it is pre- by injector performance Survey many parent BP-GA neutral nets and obtain Prediction Parameters, Prediction Parameters are outlet back pressure pc, mass ratio of induced-to-inducing air ε, by pcIt is counter to be returned One change obtains many parent BP-GA neural network prediction outlet back pressures pcActual value.
Further, in the step 2, in the step 2, the selection of node in hidden layer, learning rate takes 0.1~0.2, Training objective takes 10-3~10-6, each layer weights initial value takes the random number between [- 1,1].
Further, in the step 3, genetic algorithm initial parameter is set, makes population scale take N1, evolution number of times takes N2 Secondary, crossover probability is set to 20%~50%, and mutation probability is set to 1%~10%, each individual weights and threshold by neutral net Value composition, BP-GA neural network topology structures have l node for l-m-n, i.e. input layer, and hidden layer has m node, output layer There is n node, have l × m × n weights, m+n threshold value, so genetic algorithm individual chromosome code length is l × m × n + m+n；Initial training is carried out to the artificial neural network that step 2 is set up, chromosome is constituted with obtained weights, threshold value Body, repeats n times and obtains the individual of N group chromosomes and constitute population.
Further, in the step 4, the result of the neural network prediction constructed using every group chromosome is foundation, meter Calculate and record corresponding fitness value, predicting the outcome, its more accurate corresponding fitness value is smaller, and fitness computing formula is such as Under：
In formula, F is fitness value, and k is neutral net output node number；yiFor the expectation of i-th of node of BP neural network Output；oiFor the prediction output of i-th of node；C is coefficient.
In the step 5, selection operation is：Using roulette method selective staining body, make the chromosome that fitness value is more excellent Selected probability is bigger.
In formula：piIt is the probability for selecting i-th of chromosome；
Crossover operation is：Crossover operation is carried out using real number interior extrapolation method, four parent chromosomes are chosen herein and are intersected Operation.
Mutation operation is：Using j-th of gene a of i-th of individualijMutation operation is carried out, variation method is as follows：
aij=aij+(aij-amax) * f (g) r ＞ 0.5
aij=aij+(amin-aij)*f(g) r≤0.5
In formula, r is the random number between [0,1], amaxFor the previous of gene, aminFor the next time of gene；F (g)=rand × (1-g/Gmax)2；Rand is the random number between [0,1]；G is current iteration number of times；GmaxFor maximum evolution number of times.
In the step 6, the process that input training sample is trained to obtained many parent BP-GA neutral nets is such as Under：
6.1) output of hidden layer is calculated；
Hj=f (Sj)
Wherein l, m, n represent input layer number, node in hidden layer and output layer nodes, H respectivelyjIt is defeated for hidden layer Go out value, f (x) takes S types (Sigmoid) function for transmission function；
6.2) output of output layer is calculated
Yb=f (U)
YbFor prediction output；
6.3), when network output is not waited with desired output, there is output error e in error calculationk, formula is as follows：
ek=Yk-Ybk
YkFor desired output；
6.4) renewal of weights；
ωjk=ωjk+ηHjek
6.5) renewal of threshold value, more predicated error e update network node threshold value A, B；
Bk=Bk+ek
6.6) weights after adjusting assign many parent BP-GA neutral nets, repeat step 6.1 again) -6.5) until by mistake Difference exceedes untill setting the upper limit less than training objective or cycle-index, and many parent BP-GA neural metwork trainings are completed.
The present invention technical concept be：Hereditary (GA) algorithm be a kind of simulation nature genetic mechanism and theory of biological evolution and Into a kind of parallel Stochastic search optimization method, this method has good ability of searching optimum, quickly approaches optimal result；BP Neutral net then possesses good adaptivity and self-learning capability and good local optimal searching ability.Therefore using heredity calculation The BP neural network forecast model that method is set up, can make full use of both advantages, set up a higher injector of precision Can forecast model.
But traditional genetic algorithm, which is intragroup two chromosome of selection, carries out the dye after cross and variation is evolved Colour solid, the offspring obtained in this way is only possible to have the advantages that two parent chromosomes, present invention further propose that adopting With the artificial neural network of many parent genetic algorithms, it can be obtained by the chromosome of future generation synthesized to choosing a plurality of chromosome The advantage of a plurality of parent chromosome simultaneously therefrom selects foundation of the optimum individual for neural network prediction model, therefore can obtain More accurate result of calculation.
Based on this, the present invention proposes to predict drawing for injector using the method for the artificial neural network of many parent genetic algorithms Penetrate coefficient (ε) and outlet back pressure (pc) etc. key parameter, using this method without considering complicated flow mechanism, you can convenient fast Obtain fastly it is high-precision predict the outcome, be injector it is related manufacture and design, circulating research etc. provides foundation.
Beneficial effects of the present invention are mainly manifested in：Precision of prediction is higher, prediction is time-consuming shorter.
Brief description of the drawings
Fig. 1 is BP neural network topological structure；
Fig. 2 is many parent BP-GA neural network predictions injector performance flow charts；
Fig. 3 is many parent BP-GA neural network predictions resultant error figures.
Embodiment
The invention will be further described below in conjunction with the accompanying drawings.
1~Fig. 3 of reference picture, one kind is based on the injector performance of the artificial neural network using many parent genetic algorithms (GA) Forecasting Methodology, comprises the following steps：
Step one, the collection and processing of data
For given injector, it is citation jet body pressure (Pe), working fluid pressure (Pp), outlet to collect relevant parameter Back pressure (Pc) and mass ratio of induced-to-inducing air (ε), for accelerate neutral net convergence and reduce the training time, it is necessary to citation jet body pressure, Working fluid pressure is normalized with outlet back pressure, it is arrived between [0,1], formula is as follows：
Wherein, k is the data after normalization, and x is to be normalized data, xminTo be normalized the minimum value in data, xmaxTo be normalized maximum in data；
Step 2, the foundation of neural network structure
According to input/output argument, the input layer number of neutral net is judged as 2, and output layer nodes are also 2, The basis for selecting empirical equation of node in hidden layer takes 5, and learning rate takes 0.1, and training objective takes 0.0001, and cycle-index is set For 100 times.Each layer weights initial value takes the random number between [- 1,1].
Step 3, sets genetic algorithm initial parameter
Population scale is 10, and evolution number of times takes 50 times, and crossover probability is 40%, and mutation probability is 10%, by BP nerves The weights of network constitute the neutral net set up in genetic coding, the step 2 that reruns with threshold value and record each run completion Genetic coding set up population.
Step 4, sets up evaluation function, and the result that the neutral net constructed using every group chromosome is predicted is foundation, meter Calculate and record corresponding fitness value.
Step 5, genetic algorithm processing
A. selection operation：With certain probability selection individual for producing the next generation in old colony, it is general that individual is selected Rate is relevant with fitness value, and the value of fitness is better, and selected probability is bigger.
B. many parent crossover operations：Refer to select four parent chromosomes to carry out building for chromosome of future generation from population It is vertical, by the combined crosswise of chromosome, produce new individual.
C. mutation operation：Refer to an optional individual from colony, certain section coding in selected chromosome is become It is different to produce more excellent individual.
Step 6, the genetic coding in the chromosome newly obtained is decoded, and calculates fitness, and group enters with original seed Row compares, and selects the superior and eliminates the inferior.Four-step 5 of repeat step, is constantly selected individual in population, many parents intersect, variation Operate and record fitness value, reach the evolution number of times upper limit.The corresponding chromosome of fitness optimal solution is many fathers set up For the threshold value and weights corresponding to BP-GA neutral nets, obtained many parent BP-GA neutral nets are trained, until pre- The error for surveying output variable is less than set-point.
Step 7, in practical engineering application, is carried out with many parent BP-GA neutral nets to certain given injector performance Input variable (citation jet body pressure p under prediction, measurement virtual conditione, working fluid pressure pp), it is pre- by injector performance Survey many parent BP-GA neutral nets and obtain Prediction Parameters (outlet back pressure pc, mass ratio of induced-to-inducing air ε), by pcRenormalization is carried out to be changed Enter BP-GA neural network prediction outlet back pressures pcActual value, formula is as follows：
X=k (xmax-xmin)+xmin。
In the step 3, genetic algorithm initial parameter is set, makes population scale take 10, evolution number of times takes 50 times, intersected Probability is set to 40%, and mutation probability is set to 10%.Each individual is made up of the weights of neutral net with threshold value, due to this BP-GA Neural network topology structure is 2-5-2, i.e., input layer has 2 nodes, and output layer has 2 nodes, and hidden layer has 5 nodes, institute To have 2 × 5 × 2=20 weights, 5+2 threshold value, so this genetic algorithm individual chromosome code length is 27.To step Rapid two artificial neural networks set up carry out initial training, constitute chromosome with obtained weights, threshold value, repeat ten times To ten group chromosomes individual and constitute population.
In the step 4, evaluation function is set up.The result of the neural network prediction constructed using every group chromosome as according to According to, calculate and record corresponding fitness value, its more accurate corresponding fitness value that predicts the outcome is smaller, fitness calculating public affairs Formula is as follows：
In formula, F is fitness value, and k is neutral net output node number；yiFor the expectation of i-th of node of BP neural network Output；oiFor the prediction output of i-th of node；C is coefficient.
In the step 5, selection operation is：Using roulette method selective staining body, make the chromosome that fitness value is more excellent Selected probability is bigger.
In formula：piIt is the probability for selecting i-th of chromosome.
Many parent crossover operations are：Crossover operation is carried out using real number interior extrapolation method, four parent chromosomes are chosen herein and are entered Row crossover operation, by kth in colony1Individual chromosome ak1, kth2Individual chromosome ak2、k3Individual chromosome ak3And kth4Individual chromosome ak4 Crossover operation is carried out, method is as follows：
ak1=ak1p1+ak2p2+ak3p3+ak4p4
ak2=ak2p1+ak1p2+ak3p3+ak4p4
ak3=ak3p1+ak1p2+ak2p3+ak4p4
ak4=ak4p1+ak1p2+ak2p3+ak3p4
Wherein, p1、p2、p3And p4Be random number between [0,1] and they and be 1.
Mutation operation is：Using j-th of gene a of i-th of individualijMutation operation is carried out, variation method is as follows：
aij=aij+(aij-amax) * f (g) r ＞ 0.5
aij=aij+(amin-aij)*f(g) r≤0.5
In formula, r is the random number between [0,1], amaxFor the previous of gene, aminFor the next time of gene；F (g)=rand × (1-g/Gmax)2；Rand is the random number between [0,1]；G is current iteration number of times；GmaxFor maximum evolution number of times.
In the step 6, the genetic coding in the chromosome newly obtained is decoded, genetic algorithm meter is obtained The weights and threshold value calculated are set up neutral net and trained, and the adaptation of new individual is obtained according to the fitness computing formula of step 4 Degree, group is compared with original seed, selects the superior and eliminates the inferior.Repeat step four-five, constantly carries out cross and variation behaviour to individual in population Make, and the new fitness for producing individual is compared with former population's fitness, select the superior and eliminate the inferior, until reaching on iterations Limit.Chromosome corresponding to fitness optimal solution is decoded, weights is obtained and is used for carrying out BP-GA nerve nets with threshold value The foundation of network.
In the step 6, the process that input training sample is trained to obtained BP-GA neutral nets is as follows：
6.1) output of hidden layer is calculated.
Hj=f (Sj)
Wherein i, j, k represent input layer number, node in hidden layer and output layer nodes respectively.HjIt is defeated for hidden layer Go out value.F (x) takes S types (Sigmoid) function for transmission function.
6.2) output of output layer is calculated
Yb=f (U)
YbFor prediction output.
6.3), when network output is not waited with desired output, there is output error e in error calculationk, formula is as follows：
ek=Yk-Ybk
YkFor desired output.
6.4) renewal of weights.
ωjk=ωjk+ηHjek
6.5) renewal of threshold value, more predicated error e update network node threshold value A, B.
Bk=Bk+ek
6.6) weights after adjusting assign BP-GA neutral nets, repeat step 6.1 again) -6.5) until error is less than instruction Practice target or cycle-index exceedes untill setting the upper limit, many parent BP-GA neural metwork trainings are completed.
Example：Preferably to embody the effect of the present invention, the method for the present invention is now subjected to actual motion.Using document 3 (IW.Eames et al.A theoretical and experimental study of a small-scale steam jet refrigerator.International journal of refrigeration,18(6):378-386,1995；I.e. The theoretical and experimental study International refrigeration journals of the small steam ejector refrigeration machines such as IW.Eames, 18 (6):378-386, 1995) method in obtains 110 groups of data, randomly selects 80 groups of data according to certain rules therefrom as training sample, uses The above method carries out successive ignition training and obtains neutral net.To remaining 30 groups of data as test data, input sample is chosen This (citation jet body pressure pe, working fluid pressure pp) carry out outlet back pressure p using obtained many parent BP-GA neutral netscWith Mass ratio of induced-to-inducing air ε prediction, obtained result and result by references are compared, and assess the precision of prediction of the neutral net.
Specifically predict the outcome with literature value as shown in table 1 and Fig. 3.
Table 1
The mean error finally predicted the outcome is 0.29%, and worst error is 1.1%.Predict the outcome more accurate, use Injector mass ratio of induced-to-inducing air and Forecasting Methodology and the conventional method such as document 1 of outlet back pressure that many parent GA-BP neutral nets are completed And the method in document 2 be compared it can be found that using conventional model mean error more than in 5-10%, and worst error Up to more than 15%.It can be seen that the method for use this patent can greatly promote prediction on the premise of quick be predicted is ensured Precision.

Claims (6)

1. a kind of injector performance Forecasting Methodology of the artificial neural network based on using many parent genetic algorithms, its feature exists In：Comprise the following steps：
Step one, the collection and processing of data
For given injector, collect relevant parameter be citation jet body pressure Pe, working fluid pressure Pp, outlet back pressure Pc and Mass ratio of induced-to-inducing airCitation jet body pressure, working fluid pressure and outlet back pressure are normalized, make its to [0,1] it Between, formula is as follows：
Wherein, k is the data after normalization, and x is to be normalized data, xminTo be normalized the minimum value in data, xmaxFor It is normalized maximum in data；
Step 2, the foundation of neural network structure
According to input/output argument, the input and output layer nodal point number of neutral net is determined, the hidden layer number of plies is one layer, and node is several According to empirical equation：Draw, wherein l, n are input, output node number, and a is constant, judge the defeated of neutral net Enter node layer number, output layer nodes are chosen node in hidden layer, learning rate, training objective, cycle-index and set and each layer power It is worth initial value；
Step 3, sets genetic algorithm initial parameter
Population scale, evolution number of times, crossover probability and mutation probability are set, the weights of BP neural network and threshold value are constituted into heredity Coding, the neutral net set up in the step 2 that reruns simultaneously records the genetic coding of each run completion and sets up population；
Step 4, sets up evaluation function, and the result that the neutral net constructed using every group chromosome is predicted is calculated simultaneously as foundation Record corresponding fitness value；
Step 5, genetic algorithm processing
A. selection operation：Be used for producing the next generation with certain probability selection individual in old colony, the selected probability of individual with Fitness value is relevant, and the value of fitness is better, and selected probability is bigger；
B. many parent crossover operations：Refer to the foundation for selecting a plurality of parent chromosome to carry out chromosome of future generation from population, lead to The combined crosswise of chromosome is crossed, new individual is produced；
C. mutation operation：Refer to an optional individual from colony, certain section in selected chromosome is encoded into row variation with Produce more excellent individual；
Step 6, the genetic coding in the chromosome newly obtained is decoded, and calculates fitness, and group is compared with original seed Compared with the survival of the fittest；Four-step 5 of repeat step, is constantly selected, is intersected to individual in population, mutation operation and recorded Fitness value, reaches the evolution number of times upper limit；The corresponding chromosome of fitness optimal solution is many parent BP-GA nerves set up Obtained many parent BP-GA neutral nets are trained by threshold value and weights corresponding to network, until prediction output variable Error is less than set-point；
Step 7, in practical engineering application, is carried out in advance with many parent BP-GA neutral nets to certain given injector performance Survey, the input variable under measurement virtual condition, i.e. citation jet body pressure pe, working fluid pressure pp, pass through many parent BP-GA god Prediction Parameters are obtained through network injector performance forecast model, Prediction Parameters are outlet back pressure pc, mass ratio of induced-to-inducing airBy pcCarry out anti- Normalization obtains improving BP-GA neural network prediction outlet back pressures pcActual value.
2. the injector performance prediction side of the artificial neural network as claimed in claim 1 based on using many parent genetic algorithms Method, it is characterised in that：In the step 2, the selection of node in hidden layer, learning rate takes 0.1~0.2, and training objective takes 10-3 ~10-6, each layer weights initial value takes the random number between [- 1,1].
3. the injector performance of the artificial neural network as claimed in claim 1 or 2 based on using many parent genetic algorithms is pre- Survey method, it is characterised in that：In the step 3, genetic algorithm initial parameter is set, makes population scale take N1, evolution number of times takes N2Secondary, crossover probability is set to 20%~50%, and mutation probability is set to 1%~10%, each individual by neutral net weights with Threshold value is constituted, and BP-GA neural network topology structures have l node for l-m-n, i.e. input layer, and hidden layer has m node, output Layer has n node, has l × m × n weights, m+n threshold value, so genetic algorithm individual chromosome code length is l × m × n+m+n；Initial training is carried out to the artificial neural network that step 2 is set up, chromosome is constituted with obtained weights, threshold value Individual, repeats n times and obtains the individual of N group chromosomes and constitute population.
4. the injector performance of the artificial neural network as claimed in claim 1 or 2 based on using many parent genetic algorithms is pre- Survey method, it is characterised in that：In the step 4, the result of the neural network prediction constructed using every group chromosome as foundation, Calculate and record corresponding fitness value, its more accurate corresponding fitness value that predicts the outcome is smaller, fitness computing formula It is as follows：
In formula, F is fitness value, and k is neutral net output node number；yiFor the desired output of i-th of node of BP neural network； oiFor the prediction output of i-th of node；C is coefficient.
5. the injector performance of the artificial neural network as claimed in claim 1 or 2 based on using many parent genetic algorithms is pre- Survey method, it is characterised in that：In the step 5, selection operation is：Using roulette method selective staining body,
In formula：piIt is the probability for selecting i-th of chromosome；N is population at individual number；
Many parent crossover operations are：Crossover operation is carried out using real number interior extrapolation method, X bar parent chromosomes are chosen herein and are intersected Operation；
Mutation operation is：Using j-th of gene a of i-th of individualijMutation operation is carried out, variation method is as follows：
aij=aij+(aij-amax) * f (g) r ＞ 0.5
aij=aij+(amin-aij)*f(g) r≤0.5
In formula, r is the random number between [0,1], amaxFor the previous of gene, aminFor the next time of gene；F (g)=rand × (1- g/Gmax)2；Rand is the random number between [0,1]；G is current iteration number of times；GmaxFor maximum evolution number of times.
6. the injector performance of the artificial neural network as claimed in claim 1 or 2 based on using many parent genetic algorithms is pre- Survey method, it is characterised in that：In the step 6, the mistake that input training sample is trained to obtained BP-GA neutral nets Journey is as follows：
6.1) output of hidden layer is calculated；
Hj=f (Sj)
Wherein l, m, n represent input layer number, node in hidden layer and output layer nodes, H respectivelyjFor hidden layer output valve, F (x) takes S types (Sigmoid) function for transmission function；
6.2) output of output layer is calculated
Yb=f (U)
YbFor prediction output；
6.3), when network output is not waited with desired output, there is output error e in error calculationk, formula is as follows：
ek=Yk-Ybk
YkFor desired output；
6.4) renewal of weights；
ωjk=ωjk+ηHjek
6.5) renewal of threshold value, more predicated error e update network node threshold value A, B；
Bk=Bk+ek
6.6) weights after adjusting assign BP-GA neutral nets, repeat step 6.1 again) -6.5) until error is less than training mesh Mark or cycle-index exceed untill setting the upper limit, and many parent BP-GA neural metwork trainings are completed.
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