A MATLAB-based Study on Approximation Performances of Improved Algorithms of Typical BP Neural Networks
BP neural networks are widely used and the algorithms are various.This paper studies the advantages and disadvantages of improved algorithms of five typical BP networks,based on artificial neural network theories.First,the learning processes of improved algorithms of the five typical BP networks are elaborated on mathematically.Then a specific network is designed on the platform of MATLAB 7.0 to conduct approximation test for a given nonlinear function.At last,a comparison is made between the training speeds and memory consumption of the five BP networks.The simulation results indicate that for small scaled and medium scaled networks,LM optimization algorithm has the best approximation ability,followed by Quasi-Newton algorithm,conjugate gradient method,resilient BP algorithm,adaptive learning rate algorithm.
BP neural network Improved algorithm Function approximation MATLAB
DING Shuo WU Qing-hui
College of Engineering,Bohai University,Jinzhou,Liaoning Province 121013,China
国际会议
济南
英文
1353-1356
2012-12-29（万方平台首次上网日期，不代表论文的发表时间）