A novel neural network for associative memory via dynamical systems

K. L. Mak, J. G. Peng, Z. B. Xu, Ka Fai Cedric Yiu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

This paper proposes a novel neural network model for associative memory using dynamical systems. The proposed model is based on synthesizing the external input vector, which is different from the conventional approach where the design is based on synthesizing the connection matrix. It is shown that this new neural network (a) stores the desired prototype patterns as asymptotically stable equilibrium points, (b) has no spurious states, and (c) has learning and forgetting capabilities. Moreover, new learning and forgetting algorithms are also developed via a novel operation on the matrix space. Numerical examples are presented to illustrate the effectiveness of the proposed neural network for associative memory. Indeed, results of simulation experiments demonstrate that the neural network is effective and can be implemented easily.
Original languageEnglish
Pages (from-to)573-590
Number of pages18
JournalDiscrete and Continuous Dynamical Systems - Series B
Volume6
Issue number3
Publication statusPublished - 1 May 2006
Externally publishedYes

Keywords

  • Associative memory
  • Asymptotic stability
  • Learning and forgetting algorithm
  • Neural network
  • Prototype pattern
  • Spurious state

ASJC Scopus subject areas

  • Discrete Mathematics and Combinatorics
  • Applied Mathematics

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