Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72681
Title: Identification of nonlinear discrete-time multivariable dynamical systems by RBF neural networks
Authors: Tan, Shaohua 
Hao, Jianbin
Vandewalle, Joos
Issue Date: 1994
Source: Tan, Shaohua,Hao, Jianbin,Vandewalle, Joos (1994). Identification of nonlinear discrete-time multivariable dynamical systems by RBF neural networks. IEEE International Conference on Neural Networks - Conference Proceedings 5 : 3250-3255. ScholarBank@NUS Repository.
Abstract: In this paper, we propose a recursive identification technique for nonlinear discrete-time multivariable dynamical systems. Extending an early result to multivariable systems [8], the technique approaches a nonlinear system identification problem in two stages: One is to build up recursively a RBF (Radial-Basis-Function) neural net model structure including the size of the neural net and the parameters in the RBF neurons; the other is to design a stable recursive weight updating algorithm to obtain the weights of the net in an efficient way.
Source Title: IEEE International Conference on Neural Networks - Conference Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/72681
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

18
checked on Dec 9, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.