Singular point analysis for dynamical systems with many parameters‐an application to an asymmetrically and densely connected neural network model

Hisa‐Aki ‐A Tanaka*, Atsushi Okada, Kazuo Horiuchi, Shin'Ichi Oishi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In the nonlinear dynamical system, the singular point analysis (Painleve test) is known to be an analytic method for identifying integrable systems or characterizing chaos. In this paper, nonlinear dynamical networks, which are simplified models for mutually connected analog neurons, are studied mainly in terms of the singular point analysis by introducing the complex time. The following results were obtained: 1) some conditions for integrability and first integrals are identified; 2) as an application of Yoshida's theorem, it is proven that many cases in our system are (algebraically) noninegrable; 3) a self‐validated numerical algorithm is proposed to overcome some difficulties known to appear in applying the singular point analysis (Yoshida's theorem) to higher‐order systems.

Original languageEnglish
Pages (from-to)92-102
Number of pages11
JournalElectronics and Communications in Japan (Part III: Fundamental Electronic Science)
Volume77
Issue number10
DOIs
Publication statusPublished - 1994

Keywords

  • Singular point analysis
  • high dimensional dynamical system
  • nonlinear dynamical network
  • self‐validating numerics

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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