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Associative Memory Using Synchronization In A Chaotic Neural Network

Author

Listed:
  • Z. TAN

    (Department of Physics, The University of Lethbridge, 4401 University Dr. W. Lethbridge, Alberta T1K 3M4, Canada)

  • M. K. ALI

    (Department of Physics, The University of Lethbridge, 4401 University Dr. W. Lethbridge, Alberta T1K 3M4, Canada)

Abstract

Synchronization is introduced into a chaotic neural network model to discuss its associative memory. The relative time of synchronization of trajectories is used as a measure of pattern recognition by chaotic neural networks. The retrievability of memory is shown to be connected to synapses, initial conditions and storage capacity. The technique is simple and easy to apply to neural systems.

Suggested Citation

  • Z. Tan & M. K. Ali, 2001. "Associative Memory Using Synchronization In A Chaotic Neural Network," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 19-29.
    • Handle: RePEc:wsi:ijmpcx:v:12:y:2001:i:01:n:s0129183101001407
    DOI: 10.1142/S0129183101001407
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    Cited by:

    1. Lin, Dongyuan & Chen, Xiaofeng & Yu, Guoping & Li, Zhongshan & Xia, Yannan, 2021. "Global exponential synchronization via nonlinear feedback control for delayed inertial memristor-based quaternion-valued neural networks with impulses," Applied Mathematics and Computation, Elsevier, vol. 401(C).

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