Please use this identifier to cite or link to this item: https://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
Citation: 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

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