Please use this identifier to cite or link to this item: http://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
Source: 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
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