Please use this identifier to cite or link to this item:
|Title:||On-line stable nonlinear modelling by structurally adaptive neural nets|
|Authors:||Tan, Shaohua |
|Citation:||Tan, Shaohua,Yu, Yi (1994). On-line stable nonlinear modelling by structurally adaptive neural nets. IEEE International Conference on Neural Networks - Conference Proceedings 1 : 370-375. ScholarBank@NUS Repository.|
|Abstract:||This paper proposes a neural net based on-line scheme for modelling discrete-time multivariable nonlinear dynamical systems. Taking the advantage of structural features of RBF (Radial-Basis-Function) neural nets, the method approaches the modelling problem by setting up a coarse RBF model structure in the light of the spatial Fourier transform and spatial sampling theory, then devising appropriate on-line algorithms to carry out refinements for both the RBF net structure and the associated weights. Main convergence results are established in the paper along with the analysis backing up the structure initialization and adaptation. The effectiveness of the scheme is illustrated with an simulation example.|
|Source Title:||IEEE International Conference on Neural Networks - Conference Proceedings|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 9, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.