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

Ramiro Rico-Martinez; Ioannis G. Kevrekidis

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

Ramiro Rico-Martinez, Ioannis G. Kevrekidis

Chemical & Biological Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

31 Scopus citations

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 language	English (US)
Title of host publication	1993 IEEE International Conference on Neural Networks
Publisher	Publ by IEEE
Pages	1522-1525
Number of pages	4
ISBN (Print)	0780312007
State	Published - 1993
Event	1993 IEEE International Conference on Neural Networks - San Francisco, California, USA Duration: Mar 28 1993 → Apr 1 1993

Publication series

Name	1993 IEEE International Conference on Neural Networks

Other

Other	1993 IEEE International Conference on Neural Networks
City	San Francisco, California, USA
Period	3/28/93 → 4/1/93

All Science Journal Classification (ASJC) codes

General Engineering
Control and Systems Engineering
Software
Artificial Intelligence

Cite this

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title = "Continuous time modeling of nonlinear systems: a neural network-based approach",

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.",

author = "Ramiro Rico-Martinez and Kevrekidis, {Ioannis G.}",

year = "1993",

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note = "1993 IEEE International Conference on Neural Networks ; Conference date: 28-03-1993 Through 01-04-1993",

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T2 - 1993 IEEE International Conference on Neural Networks

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AU - Kevrekidis, Ioannis G.

PY - 1993

Y1 - 1993

N2 - 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.

AB - 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.

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UR - http://www.scopus.com/inward/citedby.url?scp=84943238797&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84943238797

SN - 0780312007

T3 - 1993 IEEE International Conference on Neural Networks

SP - 1522

EP - 1525

BT - 1993 IEEE International Conference on Neural Networks

PB - Publ by IEEE

Y2 - 28 March 1993 through 1 April 1993

ER -

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

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

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