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2013 Chaotic Hopfield Neural Network Swarm Optimization and Its Application
Yanxia Sun, Zenghui Wang, Barend Jacobus van Wyk
J. Appl. Math. 2013(SI10): 1-10 (2013). DOI: 10.1155/2013/873670

Abstract

A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.

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Yanxia Sun. Zenghui Wang. Barend Jacobus van Wyk. "Chaotic Hopfield Neural Network Swarm Optimization and Its Application." J. Appl. Math. 2013 (SI10) 1 - 10, 2013. https://doi.org/10.1155/2013/873670

Information

Published: 2013
First available in Project Euclid: 9 May 2014

zbMATH: 1266.37053
MathSciNet: MR3045408
Digital Object Identifier: 10.1155/2013/873670

Rights: Copyright © 2013 Hindawi

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