Computer Science ›› 2017, Vol. 44 ›› Issue (Z11): 51-54.doi: 10.11896/j.issn.1002-137X.2017.11A.009

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Application of BP Neural Network Based on Newly Improved Particle Swarm Optimization Algorithm in Fitting Nonlinear Function

LIN Yu-feng, DENG Hong-min and SHI Xing-yu   

  • Online:2018-12-01 Published:2018-12-01

Abstract: A newly improved particle swarm optimization algorithm for the optimization of BP neural network was introduced to solve the problem of large error in fitting the nonlinear function.In this algorithm,a new model based on the newly improved particle swarm optimization algorithm is established through respectively changing the weight non-li-nearly and learning factor linearly,then it is applied to non-linear function fitting by combining with BP neural network.The results show that the newly improved particle swarm optimization algorithm can more rationally and effectively boost the fitting ability of BP neural network,and improve the accuracy of the fitting.

Key words: BP neural network,Particle swarm optimization algorithm,Function fitting

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