DocumentCode
2361519
Title
The selection of neural models of nonlinear dynamical systems by statistical tests
Author
Urbani, D. ; Roussel-Ragot, P. ; Person, L. ; Dreyfus, G.
Author_Institution
Ecole Superieure de Phys. et de Chimie Ind., Paris, France
fYear
1994
fDate
6-8 Sep 1994
Firstpage
229
Lastpage
237
Abstract
A procedure for the selection of neural models of dynamical processes is presented. It uses statistical tests at various levels of model reduction, in order to provide optimal tradeoffs between accuracy and parsimony. The efficiency of the method is illustrated by the modeling of a highly nonlinear NARX process
Keywords
neural nets; nonlinear dynamical systems; reduced order systems; statistical analysis; efficiency; model reduction; neural models selection; nonlinear NARX process; nonlinear dynamical systems; statistical tests; Context modeling; Multi-layer neural network; Neural networks; Nonlinear dynamical systems; Polynomials; Predictive models; Recurrent neural networks; Reduced order systems; Structural engineering; System testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing [1994] IV. Proceedings of the 1994 IEEE Workshop
Conference_Location
Ermioni
Print_ISBN
0-7803-2026-3
Type
conf
DOI
10.1109/NNSP.1994.366044
Filename
366044
Link To Document