Continuous-time nonlinear signal processing: A neural network based approach for gray box identification

R. Rico-Martinez, J. S. Anderson, Yannis Kevrekidis

Research output: Contribution to conferencePaper

22 Scopus citations

Abstract

Artificial neural networks (ANNs) are often used for short term discrete time series predictions. Continuous-time models are, however, required for qualitatively correct approximations to long-term dynamics (attractors) of nonlinear dynamical systems and their transitions (bifurcations) as system parameters are varied. In previous work we developed a black-box methodology for the characterization of experimental time series as continuous-time models (sets of ordinary differential equations) based on a neural network platform. This methodology naturally lends itself to the identification of partially known first principles dynamic models, and here we present its extension to `gray-box' identification.

Original languageEnglish (US)
Pages596-605
Number of pages10
StatePublished - Dec 1 1994
EventProceedings of the 4th IEEE Workshop on Neural Networks for Signal Processing (NNSP'94) - Ermioni, GREECE
Duration: Sep 6 1994Sep 8 1994

Other

OtherProceedings of the 4th IEEE Workshop on Neural Networks for Signal Processing (NNSP'94)
CityErmioni, GREECE
Period9/6/949/8/94

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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    Rico-Martinez, R., Anderson, J. S., & Kevrekidis, Y. (1994). Continuous-time nonlinear signal processing: A neural network based approach for gray box identification. 596-605. Paper presented at Proceedings of the 4th IEEE Workshop on Neural Networks for Signal Processing (NNSP'94), Ermioni, GREECE, .