Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2013, Special Issue (2013), Article ID 873670, 10 pages.

Chaotic Hopfield Neural Network Swarm Optimization and Its Application

Yanxia Sun, Zenghui Wang, and Barend Jacobus van Wyk

Full-text: Open access

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.

Article information

Source
J. Appl. Math., Volume 2013, Special Issue (2013), Article ID 873670, 10 pages.

Dates
First available in Project Euclid: 9 May 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1399645344

Digital Object Identifier
doi:10.1155/2013/873670

Mathematical Reviews number (MathSciNet)
MR3045408

Zentralblatt MATH identifier
1266.37053

Citation

Sun, Yanxia; Wang, Zenghui; van Wyk, Barend Jacobus. Chaotic Hopfield Neural Network Swarm Optimization and Its Application. J. Appl. Math. 2013, Special Issue (2013), Article ID 873670, 10 pages. doi:10.1155/2013/873670. https://projecteuclid.org/euclid.jam/1399645344


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