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Chaotic Hopfield Neural Network Swarm Optimization and Its Application

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  • Yanxia Sun
  • Zenghui Wang
  • Barend Jacobus van Wyk

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.

Suggested Citation

  • Yanxia Sun & Zenghui Wang & Barend Jacobus van Wyk, 2013. "Chaotic Hopfield Neural Network Swarm Optimization and Its Application," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-10, April.
    • Handle: RePEc:hin:jnljam:873670
    DOI: 10.1155/2013/873670
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    1. Veerasamy, Veerapandiyan & Abdul Wahab, Noor Izzri & Ramachandran, Rajeswari & Othman, Mohammad Lutfi & Hizam, Hashim & Devendran, Vidhya Sagar & Irudayaraj, Andrew Xavier Raj & Vinayagam, Arangarajan, 2021. "Recurrent network based power flow solution for voltage stability assessment and improvement with distributed energy sources," Applied Energy, Elsevier, vol. 302(C).
    2. Yuan Gao & Yueling Guo & Nurul Atiqah Romli & Mohd Shareduwan Mohd Kasihmuddin & Weixiang Chen & Mohd. Asyraf Mansor & Ju Chen, 2022. "GRAN3SAT: Creating Flexible Higher-Order Logic Satisfiability in the Discrete Hopfield Neural Network," Mathematics, MDPI, vol. 10(11), pages 1-28, June.

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