Title :
A hybrid multimodel neural network for nonlinear systems identification
Author :
Baruch, I. ; Thomas, F. ; Garrido, R. ; Gortcheva, E.
Author_Institution :
CINVESTAV-IPN, Mexico City, Mexico
Abstract :
An improved universal parallel recurrent neural network canonical architecture, named a recurrent trainable neural network (RTNN), suited for state-space systems identification, and an improved dynamic backpropagation method of its learning, are proposed. The proposed RTNN is studied with various representative examples and the results of its learning are compared with other results given in the literature. For a complex nonlinear plants identification, a fuzzy-rule-based system and a fuzzy-neural multimodel, are used. The fuzzy-neural multimodel is applied to a mechanical system with friction identification
Keywords :
backpropagation; fuzzy neural nets; identification; large-scale systems; nonlinear systems; recurrent neural nets; state-space methods; complex systems; dynamic backpropagation; fuzzy-neural network; fuzzy-rule-based system; identification; learning; multimodel neural network; nonlinear systems; recurrent neural network; state-space; Electronic mail; Equations; Friction; Learning systems; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Recurrent neural networks; Stability; System identification;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.830854
