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
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;
Conference_Titel :
Neural Networks, 1993., IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0999-5
DOI :
10.1109/ICNN.1993.298782
