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|Title:||Nonlinear systems identification using RBF neural networks||Authors:||Tan, Shaohua
|Issue Date:||1993||Citation:||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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