Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27694
Title: System identification via a virtual higher-resolution fuzzy model
Authors: Chow, KM
Rad, AB
Keywords: Fuzzy identification
Fuzzy systems
Gradient descent method
Issue Date: 2000
Publisher: AutoSoft Press
Source: Intelligent automation and soft computing, 2000, v. 6, no. 4, p. 243-259 How to cite?
Journal: Intelligent automation and soft computing 
Abstract: A fuzzy identification algorithm with an inherent knowledge generalization mechanism is reported in this paper. In the proposed identification algorithm, a low-resolution fuzzy model is used to mimic the effect of a virtual higher-resolution model. The gradient descent optimization method is then applied to update the rule-base by using the difference between the actual system output and the model output. Simulation studies are included to demonstrate the performance of the algorithm.
URI: http://hdl.handle.net/10397/27694
ISSN: 1079-8587
EISSN: 2326-005X
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

2
Last Week
0
Last month
Citations as of Apr 30, 2016

WEB OF SCIENCETM
Citations

2
Last Week
0
Last month
0
Citations as of Aug 16, 2017

Page view(s)

31
Last Week
0
Last month
Checked on Aug 14, 2017

Google ScholarTM

Check



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