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
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.
J. Appl. Math., Volume 2013, Special Issue (2013), Article ID 873670, 10 pages.
First available in Project Euclid: 9 May 2014
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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