Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/81586
Title: New learning algorithm for RBF neural networks with applications to nonlinear system identification
Authors: Tan, Shaohua 
Hao, Jianbin
Vandewalle, Joos
Issue Date: 1995
Citation: Tan, Shaohua,Hao, Jianbin,Vandewalle, Joos (1995). New learning algorithm for RBF neural networks with applications to nonlinear system identification. Proceedings - IEEE International Symposium on Circuits and Systems 3 : 1708-1711. ScholarBank@NUS Repository.
Abstract: An identification technique is presented for nonlinear discrete-time multivariable dynamical systems based on RBF neural nets. The ways to fix the neural net structure and the weights are addressed as two different problems with separately developed on-line algorithms for their determination. Currently, the determination of the RBF net structure is still heuristics-based and this may lead to modelling error, and possible breakdown of the weight updating algorithm. Thus, there is a need to develop theory that can help aid the generation of RBF neural net structures.
Source Title: Proceedings - IEEE International Symposium on Circuits and Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/81586
ISSN: 02714310
Appears in Collections:Staff Publications

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