Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33758
Title: Function estimation using a neural-fuzzy network and an improved genetic algorithm
Authors: Lam, HK
Ling, SH
Leung, FHF 
Tam, PKS
Issue Date: 2004
Publisher: Elsevier
Source: International journal of approximate reasoning, 2004, v. 36, no. 3, p. 243-260 How to cite?
Journal: International journal of approximate reasoning 
Abstract: This paper presents the estimation of the transmission gains for an AC power line data network in an intelligent home. The estimated gains ensure the transmission reliability and efficiency. A neural-fuzzy network with rule switches is proposed to perform the estimation. An improved genetic algorithm is proposed to tune the parameters and the rules of the proposed neural-fuzzy network. By turning on or off the rule switches, an optimal rule base can be obtained. An application example will be given.
URI: http://hdl.handle.net/10397/33758
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2003.10.008
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

4
Last Week
0
Last month
0
Citations as of Aug 17, 2017

WEB OF SCIENCETM
Citations

2
Last Week
0
Last month
0
Citations as of Aug 21, 2017

Page view(s)

34
Last Week
1
Last month
Checked on Aug 21, 2017

Google ScholarTM

Check

Altmetric



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