Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72802
Title: Nonlinear systems identification using RBF neural networks
Authors: Tan, Shaohua 
Hao, Jianbin
Vandewalle, Joos
Issue Date: 1993
Source: Tan, Shaohua,Hao, Jianbin,Vandewalle, Joos (1993). Nonlinear systems identification using RBF neural networks. Proceedings of the International Joint Conference on Neural Networks 2 : 1833-1836. ScholarBank@NUS Repository.
Abstract: We present a recursive nonlinear identification technique based on feedforward neural networks. A distinct feature of the proposed technique is the use of Radial-Basis-Function (RBF) neural nets as generic discrete nonlinear model structure. RBF nets have enabled us to devise a stable weight updating algorithm that guarantees the convergence of the weights to the target values. A simulation example is provided to illustrate the effectiveness of the method.
Source Title: Proceedings of the International Joint Conference on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/72802
ISBN: 0780314212
Appears in Collections:Staff Publications

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