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

R. Horie, E. Aiyoshi

研究成果: Conference article査読

4 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)1886-1891
ページ数6
ジャーナルProceedings of the IEEE International Conference on Systems, Man and Cybernetics
2
出版ステータスPublished - 1998 12 1
外部発表はい
イベントProceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 2 (of 5) - San Diego, CA, USA
継続期間: 1998 10 111998 10 14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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