Please use this identifier to cite or link to this item:
|Title:||On-line approach to nonlinear system identification using structure-adaptive neural networks|
|Authors:||Tan, Shaohua |
|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)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 8, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.