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Proceedings Paper

An improved particle swarm optimization based training algorithm for neural network
Author(s): Fuqing Zhao; Yi Hong; Dongmei Yu; Yahong Yang
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Paper Abstract

The Particle Swarm Optimization (PSO) method was originally designed by Kennedy and Eberhart in 1995 and has been applied successfully to various optimization problems. The PSO idea is inspired by natural concepts such as fish schooling, bird flocking and human social relations. Backpropagation (BP) is generally used for neural network training. It is very important to choose a proper algorithm for training a neural network. In this paper, we present a modified particle swarm optimization based training algorithm for neural network. The proposed method modify the trajectories (positions and velocities) of the particle based on the best positions visited earlier by themselves and other particles, and also incorporates population diversity method to avoid premature convergence. Experimental results have demonstrated that the modified PSO is a useful tool for training neural network.

Paper Details

Date Published: 20 February 2006
PDF: 6 pages
Proc. SPIE 6041, ICMIT 2005: Information Systems and Signal Processing, 604102 (20 February 2006); doi: 10.1117/12.664276
Show Author Affiliations
Fuqing Zhao, Lanzhou Univ. of Technology (China)
Yi Hong, Lanzhou Univ. of Technology (China)
Dongmei Yu, Lanzhou Univ. of Technology (China)
Yahong Yang, Lanzhou Univ. of Technology (China)

Published in SPIE Proceedings Vol. 6041:
ICMIT 2005: Information Systems and Signal Processing
Yunlong Wei; Kil To Chong; Takayuki Takahashi, Editor(s)

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