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|Title:||Nonlinear systems identification using RBF neural networks|
|Authors:||Tan, Shaohua |
|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|
|Appears in Collections:||Staff Publications|
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