CN109002878A - A kind of GA Optimized BP Neural Network - Google Patents

A kind of GA Optimized BP Neural Network Download PDF

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CN109002878A
CN109002878A CN201810727710.2A CN201810727710A CN109002878A CN 109002878 A CN109002878 A CN 109002878A CN 201810727710 A CN201810727710 A CN 201810727710A CN 109002878 A CN109002878 A CN 109002878A
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neural network
individual
fitness
chromosome
formula
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俞阿龙
诸飞
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Huaiyin Normal University
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Abstract

A kind of GA Optimized BP Neural Network, it is related to a kind of BP neural network technical field.It is comprised the following steps: step 1, initialization population select suitable coding mode；The selection of step 2, fitness function；Step 3, selection operation use roulette method；Step 4, crossover operation；Step 5, mutation operation；New chromosome is replaced original chromosome by step 6, is calculated fitness and is otherwise gone to step 3 if meeting condition jumps to step 7 and continue to optimize；Best initial weights and threshold values are assigned to BP neural network and are used to train by step 7, the error amount until reaching setting；Step 8, the evaluation that trained BP neural network is used for plant effuent matter, obtain evaluation result.After adopting the above technical scheme, the invention has the following beneficial effects: can effectively solve that BP network easily sinks into the slow even not convergence problem of local minimum, convergence rate, and hidden layer neuron number can be reasonably selected.

Description

A kind of GA Optimized BP Neural Network
Technical field
The present invention relates to BP neural network technical fields, and in particular to a kind of GA Optimized BP Neural Network.
Background technique
Although BP neural network has very strong nonlinearity mapping ability and simple network structure.But BP neural network is also deposited In many disadvantages: hidden layer neuron number is difficult to determine, increases calculation amount too much, influence convergence rate, is difficult to very little accurately Prediction；Algorithm the convergence speed is slow；It is easily trapped into locally optimal solution.
The natural selection of genetic algorithm (Genetic Algorithm) genetic manipulation and " survival of the fittest " is come guidance learning With determine the direction of search, the topological structure and its weight and threshold values of network can be optimized simultaneously during Optimized BP Neural Network, Select network model according to sample knowledge.And change with the complexity of problem, the dynamic of BP network may be implemented Adaptivity.GA has of overall importance, concurrency, rapidity, well adapting to property and robustness, can effectively solve that BP network is easy Sink into the slow even not convergence problem of local minimum, convergence rate, and hidden layer neuron number can be reasonably selected.
Summary of the invention
In view of the defects and deficiencies of the prior art, the present invention intends to provide a kind of GA Optimized BP Neural Network, energy Effective solution BP network easily sinks into the slow even not convergence problem of local minimum, convergence rate, and can reasonably select hidden layer mind Through first number.
To achieve the above object, the present invention is using following technical scheme: it is comprised the following steps:
Step 1, initialization population select suitable coding mode；
The selection of step 2, fitness function obtains the initial weight and threshold values of BP neural network according to individual, with training Data training BP neural network after forecasting system export, using prediction output desired output between Error Absolute Value and E as Ideal adaptation angle value F, calculation formula are as follows:
In formula, n is network output node number, and yi is the desired output of i-th of node of BP neural network, and oi is i-th of section The prediction output of point, k is coefficient；
Step 3, selection operation use roulette method, that is, are based on fitness ratio strategy, the select probability Pi of each individual i Are as follows:
fi=k/Fi (2)
In formula, Fi is the fitness of individual i, since fitness value is the smaller the better, so to fitness before individual choice It is worth inverted, N is population invariable number, and k is coefficient；
Step 4, crossover operation, since individual uses real coding, so crossover operation method uses real number interior extrapolation method, the K chromosome ak and first of chromosome al are as follows in j crossover operations:
In formula, b is the random number between [0,1]；
Step 5, mutation operation, j-th of gene aij for choosing i-th of individual make a variation, and mutation operation method is as follows:
In formula, amax is the upper bound of gene aij, and amin is the lower bound of gene aij, and f (g)=r2 (1-g/Gmax) 2, r2 is One random number, g are current iteration number, and Gmax is maximum evolution number, random number of the r between [0,1]；
New chromosome is replaced original chromosome by step 6, fitness is calculated, if meeting condition jumps to step 7, otherwise Step 3 is gone to continue to optimize；
Best initial weights and threshold values are assigned to BP neural network and are used to train by step 7, the error amount until reaching setting；
Step 8, the evaluation that trained BP neural network is used for plant effuent matter, obtain evaluation result.
The coding mode uses real coding.
The working principle of the invention: being searched in global scope, optimized first the weight and threshold values of BP using GA, then will Best initial weights and threshold values are assigned to BP and are trained, and training reaches the error of setting, and training is completed, and carry out simulation and prediction.
After adopting the above technical scheme, the invention has the following beneficial effects: can effectively solve BP network easily sink into local minimum, The slow even not convergence problem of convergence rate, and hidden layer neuron number can be reasonably selected.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this Some embodiments of invention without any creative labor, may be used also for those of ordinary skill in the art To obtain other drawings based on these drawings.
Fig. 1 is schematic process flow diagram of the invention.
Specific embodiment
Referring to shown in Fig. 1, present embodiment the technical solution adopted is that: it is comprised the following steps:
Step 1, initialization population select suitable coding mode；
The selection of step 2, fitness function obtains the initial weight and threshold values of BP neural network according to individual, with training Data training BP neural network after forecasting system export, using prediction output desired output between Error Absolute Value and E as Ideal adaptation angle value F, calculation formula are as follows:
In formula, n is network output node number, and yi is the desired output of i-th of node of BP neural network, and oi is i-th of section The prediction output of point, k is coefficient；
Step 3, selection operation use roulette method, that is, are based on fitness ratio strategy, the select probability Pi of each individual i Are as follows:
fi=k/Fi (7)
In formula, Fi is the fitness of individual i, since fitness value is the smaller the better, so to fitness before individual choice It is worth inverted, N is population invariable number, and k is coefficient；
Step 4, crossover operation, since individual uses real coding, so crossover operation method uses real number interior extrapolation method, the K chromosome ak and first of chromosome al are as follows in j crossover operations:
In formula, b is the random number between [0,1]；
Step 5, mutation operation, j-th of gene aij for choosing i-th of individual make a variation, and mutation operation method is as follows:
In formula, amax is the upper bound of gene aij, and amin is the lower bound of gene aij, and f (g)=r2 (1-g/Gmax) 2, r2 is One random number, g are current iteration number, and Gmax is maximum evolution number, random number of the r between [0,1]；
New chromosome is replaced original chromosome by step 6, fitness is calculated, if meeting condition jumps to step 7, otherwise Step 3 is gone to continue to optimize；
Best initial weights and threshold values are assigned to BP neural network and are used to train by step 7, the error amount until reaching setting；
Step 8, the evaluation that trained BP neural network is used for plant effuent matter, obtain evaluation result.
The coding mode uses real coding.For the particularity of trade effluent, convenient for the genetic search of larger space, Genetic algorithm required precision is improved, the complexity of coding and decoding is avoided.
The working principle of the invention: being searched in global scope, optimized first the weight and threshold values of BP using GA, then will Best initial weights and threshold values are assigned to BP and are trained, and training reaches the error of setting, and training is completed, and carry out simulation and prediction.
After adopting the above technical scheme, the invention has the following beneficial effects: can effectively solve BP network easily sink into local minimum, The slow even not convergence problem of convergence rate, and hidden layer neuron number can be reasonably selected.
The above is only used to illustrate the technical scheme of the present invention and not to limit it, and those of ordinary skill in the art are to this hair The other modifications or equivalent replacement that bright technical solution is made, as long as it does not depart from the spirit and scope of the technical scheme of the present invention, It is intended to be within the scope of the claims of the invention.

Claims (2)

1. a kind of GA Optimized BP Neural Network, it is characterised in that it is comprised the following steps:
Step 1, initialization population select suitable coding mode；
The selection of step 2, fitness function obtains the initial weight and threshold values of BP neural network according to individual, uses training data Forecasting system exports after training BP neural network, using the Error Absolute Value and E predicted between output and desired output as individual Fitness value F, calculation formula are as follows:
In formula, n is network output node number, and yi is the desired output of i-th of node of BP neural network, and oi is i-th of node Prediction output, k is coefficient；
Step 3, selection operation use roulette method, that is, are based on fitness ratio strategy, the select probability Pi of each individual i are as follows:
fi=k/Fi (2)
In formula, Fi is the fitness of individual i, since fitness value is the smaller the better, so taking before individual choice to fitness value Inverse, N are population invariable number, and k is coefficient；
Step 4, crossover operation, since individual uses real coding, so crossover operation method use real number interior extrapolation method, k-th Chromosome ak and first of chromosome al are as follows in j crossover operations:
In formula, b is the random number between [0,1]；
Step 5, mutation operation, j-th of gene aij for choosing i-th of individual make a variation, and mutation operation method is as follows:
In formula, amax is the upper bound of gene aij, and amin is the lower bound of gene aij, and f (g)=r2 (1-g/Gmax) 2, r2 is one Random number, g are current iteration number, and Gmax is maximum evolution number, random number of the r between [0,1]；
New chromosome is replaced original chromosome by step 6, is calculated fitness and is otherwise gone to if meeting condition jumps to step 7 Step 3 continues to optimize；
Best initial weights and threshold values are assigned to BP neural network and are used to train by step 7, the error amount until reaching setting；
Step 8, the evaluation that trained BP neural network is used for plant effuent matter, obtain evaluation result.
2. a kind of GA Optimized BP Neural Network according to claim 1, it is characterised in that: the coding mode uses real number Coding.
CN201810727710.2A 2018-07-05 2018-07-05 A kind of GA Optimized BP Neural Network Pending CN109002878A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109726817A (en) * 2018-12-21 2019-05-07 河北工业大学 The WPT system impedance matching methods of genetic algorithm optimization BP neural network
CN110084354A (en) * 2019-04-09 2019-08-02 浙江工业大学 A method of based on genetic algorithm training ANN Control game role behavior
CN111898827A (en) * 2020-08-03 2020-11-06 西安石油大学 Motorcycle helmet wearing condition prediction evaluation model based on neural network

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103699873A (en) * 2013-09-22 2014-04-02 杭州电子科技大学 Lower-limb flat ground walking gait recognition method based on GA-BP (Genetic Algorithm-Back Propagation) neural network
CN104820977A (en) * 2015-05-22 2015-08-05 无锡职业技术学院 BP neural network image restoration algorithm based on self-adaption genetic algorithm
CN106482938A (en) * 2016-10-14 2017-03-08 温州大学 Brake fluid system multi-source fusion fault predicting method based on GA BP network

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103699873A (en) * 2013-09-22 2014-04-02 杭州电子科技大学 Lower-limb flat ground walking gait recognition method based on GA-BP (Genetic Algorithm-Back Propagation) neural network
CN104820977A (en) * 2015-05-22 2015-08-05 无锡职业技术学院 BP neural network image restoration algorithm based on self-adaption genetic algorithm
CN106482938A (en) * 2016-10-14 2017-03-08 温州大学 Brake fluid system multi-source fusion fault predicting method based on GA BP network

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘浩然 等: "一种基于改进遗传算法的神经网络优化算法研究", 《仪器仪表学报》 *
周德红 等: "基于GA-BP神经网络的化工园区应急救援能力", 《安全与环境工程》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109726817A (en) * 2018-12-21 2019-05-07 河北工业大学 The WPT system impedance matching methods of genetic algorithm optimization BP neural network
CN110084354A (en) * 2019-04-09 2019-08-02 浙江工业大学 A method of based on genetic algorithm training ANN Control game role behavior
CN111898827A (en) * 2020-08-03 2020-11-06 西安石油大学 Motorcycle helmet wearing condition prediction evaluation model based on neural network

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