The Development of Neural Network Models by Revised Particle Swarm Optimization

DOI

10.2991/jcis.2006.138How to use a DOI?

Keywords

neural networks, particle swarm optimization (PSO), mutation, re-seeding.

Abstract

A novel training paradigm for artificial neural networks had been developed and presented in this article. In the proposed approach, a revised version of particle swarm optimization (PSO) had been employed to find out the optimal connection weights of feed-forward artificial neural networks for given training sets. Literatures reported that conventional particle swarm optimization could easily get stuck at local optima, especially in problem domains with high dimensionality. In our scheme, a re-seeding mechanism will be invoked when the system is under the risk of converging to pre-mature solutions. The incorporation of the concept of mutation had endowed the systems with better capability in escaping local optima and approaching to the global optimum. A series of experiments were conducted to verify the feasibility and effectiveness of the proposed approach, and optimistic results were obtained as expected. In additions, the impact and influence of different parameter settings on system performance was investigated through comprehensive empirical study, as reported in this paper.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Peitsang Wu
AU  - Chin-Shiuh Shieh
AU  - Jar-Her Kao
PY  - 2006/10
DA  - 2006/10
TI  - The Development of Neural Network Models by Revised Particle Swarm Optimization
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.138
DO  - 10.2991/jcis.2006.138
ID  - Wu2006/10
ER  -