Neural Networks realization of searching models for Nash Equilibrium points and their application to associative memories

R. Horie, E. Aiyoshi

Research output: Contribution to journalConference article

4 Citations (Scopus)

Abstract

We propose a new mutually coupled plural Neural Networks (N.N.) modules and its application to associative memories from the view point of noncooperative game theory. First, We propose a new dynamical searching model named Parallel Steepest Descent Method with Braking operators (PSDMB) which searches the Nash Equilibrium (NE) points under [0, 1]-interval or nonnegative constraints. Second, we propose a new mutually coupled plural N.N. modules named Game Neural Networks (GNN) to realize the proposed PSDMB with quadratic objective functions. In Addition, we indicate relations between the PSDMB, the GNN and the Lotka-Volterra equation. Last, for an application of the proposed GNN, we propose two kinds of multi modular associative memories which can associate the combined patterns composed of plural partial patterns: (1) the combined patterns are stored as the NE points and robust for noisy inputs; (2) the circulative sequence of the combined patterns are stored as saddles of a heteroclinic cycle.

Original languageEnglish
Pages (from-to)1886-1891
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume2
Publication statusPublished - 1998 Dec 1
EventProceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 2 (of 5) - San Diego, CA, USA
Duration: 1998 Oct 111998 Oct 14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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