DocumentCode
1908054
Title
Continuous time modeling of nonlinear systems: a neural network-based approach
Author
Rico-Martínez, Ramiro ; Kevrekidis, Ioannis G.
Author_Institution
Dept. of Chem. Eng., Princeton Univ., NJ, USA
fYear
1993
fDate
1993
Firstpage
1522
Abstract
A neural network-based approach for continuous-time modeling of nonlinear systems is presented. The approach is based on an implicit integrator and recurrent networks. The resulting continuous-time model (a set of ordinary differential equations) is capable of correctly capturing the long term attractors of the system
Keywords
differential equations; modelling; nonlinear systems; recurrent neural nets; continuous-time modeling; differential equations; integrator; neural network; nonlinear systems; recurrent networks; Artificial neural networks; Bifurcation; Chemical engineering; Current measurement; Delay effects; Extraterrestrial measurements; Neural networks; Nonlinear systems; Predictive models; Time measurement;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993., IEEE International Conference on
Conference_Location
San Francisco, CA
Print_ISBN
0-7803-0999-5
Type
conf
DOI
10.1109/ICNN.1993.298782
Filename
298782
Link To Document