DocumentCode
2885770
Title
Nonlinear dynamic system identification using radial basis function networks
Author
Ni, Xianfeng ; Simons, Stef J R
Author_Institution
Dept. of Chem. & Biochem. Eng., Univ. Coll. London, UK
Volume
1
fYear
1996
fDate
11-13 Dec 1996
Firstpage
935
Abstract
A radial basis function (RBF) network is used to approximate a continuous nonlinear dynamic system described by a set of nonlinear state equations. The learning laws to adjust the network weight parameters are derived using a Lyapunov function, such that the convergence is guaranteed. Simulations show that the method is simple and extremely effective. The robustness of the approach, with respect to the parameters and implementation, is considered in the paper
Keywords
Lyapunov matrix equations; continuous time systems; feedforward neural nets; identification; nonlinear dynamical systems; Lyapunov function; continuous nonlinear dynamic system; convergence; identification; learning laws; nonlinear state equations; radial basis function networks; Differential equations; Lyapunov method; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Radial basis function networks; State estimation; Symmetric matrices; System identification; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location
Kobe
ISSN
0191-2216
Print_ISBN
0-7803-3590-2
Type
conf
DOI
10.1109/CDC.1996.574580
Filename
574580
Link To Document