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https://scholarbank.nus.edu.sg/handle/10635/62544
Title: | On-line approach to nonlinear system identification using structure-adaptive neural networks | Authors: | Tan, Shaohua Yu, Yi Vandewalle, Joos |
Issue Date: | 1994 | Citation: | Tan, Shaohua,Yu, Yi,Vandewalle, Joos (1994). On-line approach to nonlinear system identification using structure-adaptive neural networks. Artificial Neural Networks in Engineering - Proceedings (ANNIE'94) 4 : 801-806. ScholarBank@NUS Repository. | Abstract: | An on-line nonlinear system identification scheme is proposed based on the idea of neural nets structure adaptation. Using the so-called RBF (Radial-Basis-Function) neural nets as generic model structure, we have been able to derive a stable and efficient approach including the structural generation, grid adaptation and the weight update. Main convergence results are established in the paper along with the analysis backing up the on-line model formation. Simulation analysis is used to evaluate the effectiveness of the scheme. | Source Title: | Artificial Neural Networks in Engineering - Proceedings (ANNIE'94) | URI: | http://scholarbank.nus.edu.sg/handle/10635/62544 |
Appears in Collections: | Staff Publications |
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