Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11699
Title: Nonlinear system modeling via knot-optimizing B-spline networks
Authors: Yiu, KFC
Wang, S
Teo, KL
Tsoi, AC
Keywords: B-splines
Knot points
Neural network
Nonlinear system modeling
Issue Date: 2001
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks, 2001, v. 12, no. 5, p. 1013-1022 How to cite?
Journal: IEEE transactions on neural networks 
Abstract: In using the B-spline network for nonlinear system modeling, owing to a lack of suitable theoretical results, it is quite difficult to choose an appropriate set of knot points to achieve a good network structure for minimizing, say, a minimum error criterion. In this paper, a novel knot-optimizing B-spline network is proposed to approximate general nonlinear system behavior. The knot points are considered to be independent variables in the B-spline network and are optimized together with the B-spline expansion coefficients. A simulated annealing algorithm with an appropriate search strategy is used as an optimization algorithm for the training process in order to avoid any possible local minima. Examples involving dynamic systems up to six dimensions in the input space to the network are solved by the proposed method to illustrate the effectiveness of this approach.
URI: http://hdl.handle.net/10397/11699
ISSN: 1045-9227
DOI: 10.1109/72.950131
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

23
Last Week
0
Last month
0
Citations as of Sep 15, 2017

WEB OF SCIENCETM
Citations

20
Last Week
0
Last month
0
Citations as of Sep 14, 2017

Page view(s)

37
Last Week
4
Last month
Checked on Sep 18, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.