A MATLAB-based Study on Approximation Performances of Improved Algorithms of Typical BP Neural Networks

摘要

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.