An efficient evolutionary algorithm for fuzzy inference systems

Lin, CJ; Chen, CH; Lin, CT

An efficient evolutionary algorithm for fuzzy inference systems

Lin, CJ Chen, CH Lin, CT

Permalink

Publication Type:: Journal Article
Citation:: Evolving Systems, 2011, 2 (2), pp. 83 - 99
Issue Date:: 2011-06-01

Closed Access

	Filename	Description	Size
	10.1007_s12530-010-9024-8.pdf	Published Version	931.62 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lin, CJ	en_US
dc.contributor.author	Chen, CH	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2011-06-01	en_US
dc.identifier.citation	Evolving Systems, 2011, 2 (2), pp. 83 - 99	en_US
dc.identifier.issn	1868-6478	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116308
dc.description.abstract	In this paper, a novel self-constructing evolutionary algorithm (SCEA) for designing a TSK-type fuzzy model (TFM) is proposed. The proposed SCEA method is different from normal genetic algorithms (GAs). A chromosome of a population in traditional GAs represents a full solution and only one population presents all solutions in each generation. Our proposed method uses a population to evaluate a partial solution locally and applies several populations to construct a full solution. Thus, a chromosome represents only a partial solution. The proposed SCEA method uses the self-constructing learning algorithm to construct the TFM automatically that is based on the input training data to decide on the input partition. Fuzzy rules are created and begin to grow as the first training pattern arrives. Thus, the user need not give any a priori knowledge or even any initial information on the SCEA. We also adopted the sequence search-based dynamic evolution (SSDE) method to carry out parameter learning of the TFM. Simulation results have shown that the proposed SCEA method performs better than some existing methods. © 2010 Springer-Verlag.	en_US
dc.relation.ispartof	Evolving Systems	en_US
dc.relation.isbasedon	10.1007/s12530-010-9024-8	en_US
dc.title	An efficient evolutionary algorithm for fuzzy inference systems	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

In this paper, a novel self-constructing evolutionary algorithm (SCEA) for designing a TSK-type fuzzy model (TFM) is proposed. The proposed SCEA method is different from normal genetic algorithms (GAs). A chromosome of a population in traditional GAs represents a full solution and only one population presents all solutions in each generation. Our proposed method uses a population to evaluate a partial solution locally and applies several populations to construct a full solution. Thus, a chromosome represents only a partial solution. The proposed SCEA method uses the self-constructing learning algorithm to construct the TFM automatically that is based on the input training data to decide on the input partition. Fuzzy rules are created and begin to grow as the first training pattern arrives. Thus, the user need not give any a priori knowledge or even any initial information on the SCEA. We also adopted the sequence search-based dynamic evolution (SSDE) method to carry out parameter learning of the TFM. Simulation results have shown that the proposed SCEA method performs better than some existing methods. © 2010 Springer-Verlag.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/116308