Continuous time modeling of nonlinear systems: a neural network-based approach

Ramiro Rico-Martinez, Yannis Kevrekidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a neural network-based approach for continuous-time modeling of nonlinear systems. In a previous paper a similar goal was achieved by using feedforward networks and an explicit integrator scheme; the approach presented here is based on a implicit integrator and recurrent networks. The resulting continuous-time model (a set of ODEs) is capable of correctly capturing the long term attractors of the system.

Original languageEnglish (US)
Title of host publication1993 IEEE International Conference on Neural Networks
PublisherPubl by IEEE
Pages1522-1525
Number of pages4
ISBN (Print)0780312007
StatePublished - Jan 1 1993
Event1993 IEEE International Conference on Neural Networks - San Francisco, California, USA
Duration: Mar 28 1993Apr 1 1993

Publication series

Name1993 IEEE International Conference on Neural Networks

Other

Other1993 IEEE International Conference on Neural Networks
CitySan Francisco, California, USA
Period3/28/934/1/93

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Rico-Martinez, R., & Kevrekidis, Y. (1993). Continuous time modeling of nonlinear systems: a neural network-based approach. In 1993 IEEE International Conference on Neural Networks (pp. 1522-1525). (1993 IEEE International Conference on Neural Networks). Publ by IEEE.