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Optimized BP Neural Network Model Based on Niche Genetic Algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 223)

Abstract

According to the shortcomings of BP neural network model, such as slower convergence speed, entrapment in local optimum, unstable network structure etc., and an improved BP neural network model based on niche genetic algorithm (NGA-BP) was presented. The proposed model first makes full use of the global searching ability of genetic algorithm and the nonlinear reflection ability and the association learning ability of BP neural network to optimize the initial connection weights and thresholds of the neural network by means of selection operation, crossover operation, mutation operation and niche pass, and then adopts BP algorithm to train network, which can effectively solve the problems of BP network about unreasonable initial value and network nonconvergence, and improve the convergence speed and the stability of network. The experimental results show that the model is more feasible and effective than the traditional methods.

Keywords

BP neural network Niche genetic algorithms Nonlinear reflection Genetic operations 

Notes

Acknowledgments

This work is supported by the Foundation and Frontier Technologies Research Plan Projects of Henan Province of China (No. 102300410266 and No. 122300410287) and a grant from the Ph.D. Research Funded Projects of Zhengzhou University of Light Industry (No. 2010BSJJ038). In addition, this work also received guidance from Huang De-Shuang who is a distinguished professor in Henan Province.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.No.203, School of Computer and Communication EngineeringZhengzhou University of Light IndustryZhengzhouChina

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