• DocumentCode
    1469617
  • Title

    PSO-Based Cloning Template Design for CNN Associative Memories

  • Author

    Giaquinto, A. ; Fornarelli, G.

  • Author_Institution
    Dipt. di Elettrotec. ed Elettron., Politec. di Bari, Bari, Italy
  • Volume
    20
  • Issue
    11
  • fYear
    2009
  • Firstpage
    1837
  • Lastpage
    1841
  • Abstract
    In this brief, a synthesis procedure for cellular neural networks (CNNs) with space-invariant cloning templates is proposed. The design algorithm is based on the use of the evolutionary algorithm of the particle swarm optimization (PSO) with the application to associative memories. The proposed synthesis procedure takes into account requirements in terms of robustness to parametric variations. Numerical results show that the networks also guarantee good performances in terms of correct recall in the presence of noisy patterns.
  • Keywords
    cellular neural nets; content-addressable storage; evolutionary computation; particle swarm optimisation; PSO; associative memory; cellular neural network; evolutionary algorithm; parametric variation; particle swarm optimization; space-invariant cloning template design; Associative memories; cellular neural networks (CNNs); particle swarm optimization (PSO); robustness to parametric variations; Algorithms; Artificial Intelligence; Association Learning; Computer Simulation; Mathematical Computing; Mathematical Concepts; Memory; Neural Networks (Computer);
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2009.2031870
  • Filename
    5262979