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
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;
Conference_Titel :
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-3590-2
DOI :
10.1109/CDC.1996.574580
