Research of training feedforward neural networks based on hybrid chaos particle swarm optimization-back-propagation

Abstract

This paper proposed a new method to train feedforward neural networks(FNNs) parameters based on the iterative chaotic map with infinite collapses particle swarm optimization(ICMICPSO) algorithm. This algorithm made full use of the information of BP's error back propagation and gradient. It used ICMICPS as the global optimizer to adjust the neural networks' weights and thresholds, when network parameters converge around global optimum. And it used gradient information as a local optimizer to accelerate the modification at a local scale. Compared with other algorithms, results show that the performance of the ICMICPSO-BPNN method is superior to the contrast methods in training and generalization ability. © 2014 Springer International Publishing Switzerland.