Noninvertibility and resonance in discrete-time neural networks for time-series processing

N. Gicquel, J. S. Anderson, L. G. Kevrekidis

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

We present a computer-assisted study emphasizing certain elements of the dynamics of artificial neural networks (ANNs) used for discrete time-series processing and nonlinear system identification. The structure of the network gives rise to the possibility of multiple inverses of a phase point backward in time; this is not possible for the continuous-time system from which the time series are obtained. Using a two-dimensional illustrative model in an oscillatory regime, we study here the interaction of attractors predicted by the discrete-time ANN model (invariant circles and periodic points locked on them) with critical curves. These curves constitute a generalization of critical points for maps of the interval (in the sense of Julia-Fatou); their interaction with the model-predicted attractors plays a crucial role in the organization of the bifurcation structure and ultimately in determining the dynamic behavior predicted by the neural network.

Original languageEnglish (US)
Pages (from-to)8-18
Number of pages11
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Volume238
Issue number1
DOIs
StatePublished - Jan 26 1998

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Keywords

  • Critical curves
  • Neural networks
  • Noninvertibility
  • System identification

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