Comparison of Particle Swarm Optimization and Backpropagation as Training Algorithms for Neural Networks
This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/669
There were 207 downloads as of 22 Jun 2016.
Abstract
Particle swarm optimization (PSO) motivated by the social behavior of organisms, is a step up to existing evolutionary algorithms for optimization of continuous nonlinear functions. Backpropagation (BP) is generally used for neural network training. Choosing a proper algorithm for training a neural network is very important. In this paper, a comparative study is made on the computational requirements of the PSO and BP as training algorithms for neural networks. Results are presented for a feedforward neural network learning a nonlinear function and these results show that the feedforward neural network weights converge faster with the PSO than with the BP algorithm.