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|Title:||Identification of nonlinear discrete-time multivariable dynamical systems by RBF neural networks||Authors:||Tan, Shaohua
|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 , 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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