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Associative Memories Based on Discrete-Time Cellular Neural Networks with One-Dimensional Space-Invariant Templates

  • Zhigang Zeng
  • Jun Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

In this paper, discrete-time cellular neural networks with one-dimensional space invariant are designed to associative memories. The obtained results enable both heteroassociative and autoassociative memories to be synthesized by assuring the global asymptotic stability of the equilibrium point and the feeding data via external inputs rather than initial conditions. It is shown that criteria herein can ensure the designed input matrix to be obtained by using one-dimensional space-invariant cloning template. Finally, one specific example is included to demonstrate the applicability of the methodology.

Keywords

Associative Memory Recurrent Neural Network Cellular Neural Network Global Asymptotic Stability Global Exponential Stability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhigang Zeng
    • 1
    • 2
  • Jun Wang
    • 2
  1. 1.School of AutomationWuhan University of TechnologyWuhanChina
  2. 2.Department of Automation and Computer-Aided EngineeringThe Chinese University of Hong KongShatin, New Territories, Hong Kong

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