Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification

Pratama, M; Er, M; Li, X; San, L; Richard, JO; Zhai, L; Torabi, A; Arifin, I

Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification

Pratama, M Er, M Li, X San, L Richard, JO Zhai, L Torabi, A Arifin, I

Permalink

Publisher:: Springer Berlin / Heidelberg
Publication Type:: Conference Proceeding
Citation:: Advances in Neural Networks ISNN 2011 Lecture Notes in Computer Science, 2011, 6676 pp. 525 - 534
Issue Date:: 2011-01

Closed Access

	Filename	Description	Size
	2013006249OK.pdf		334.92 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	Pratama, M	en_US
dc.contributor.author	Er, M	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	San, L	en_US
dc.contributor.author	Richard, JO	en_US
dc.contributor.author	Zhai, L	en_US
dc.contributor.author	Torabi, A	en_US
dc.contributor.author	Arifin, I	en_US
dc.contributor.editor	NA	en_US
dc.date	2011-05-29	en_US
dc.date.issued	2011-01	en_US
dc.identifier.citation	Advances in Neural Networks ISNN 2011 Lecture Notes in Computer Science, 2011, 6676 pp. 525 - 534	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29301
dc.description.abstract	This paper discusses an optimization of Dynamic Fuzzy Neural Network (DFNN) for nonlinear system identification. DFNN has 10 parameters which are proved sensitive to the performance of that algorithm. In case of not suitable parameters, the result gives undesirable of the DFNN. In the other hand, each of problems has different characteristics such that the different values of DFNN parameters are necessary. To solve that problem is not able to be approached with trial and error, or experiences of the experts. Therefore, more scientific solution has to be proposed thus DFNN is more user friendly, Genetic Algorithm overcomes that problems. Nonlinear system identification is a common testing of Fuzzy Neural Network to verify whether FNN might achieve the requirement or not. The Experiments show that Genetic Dynamic Fuzzy Neural Network Genetic (GDFNN) exhibits the best result which is compared with other methods.	en_US
dc.publisher	Springer Berlin / Heidelberg	en_US
dc.relation.ispartof	Advances in Neural Networks ISNN 2011 Lecture Notes in Computer Science	en_US
dc.relation.ispartof	8th International Symposium on Neural Networks	en_US
dc.relation.isbasedon	10.1007/978-3-642-21090-7_61	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification	en_US
dc.type	Conference Proceeding
utslib.citation.volume	6676	en_US
utslib.location	China	en_US
utslib.location.activity	Guilin, China	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Guilin, China	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
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	China	en_US
pubs.start-date	2011-05-29	en_US
pubs.volume	6676	en_US

Abstract:

This paper discusses an optimization of Dynamic Fuzzy Neural Network (DFNN) for nonlinear system identification. DFNN has 10 parameters which are proved sensitive to the performance of that algorithm. In case of not suitable parameters, the result gives undesirable of the DFNN. In the other hand, each of problems has different characteristics such that the different values of DFNN parameters are necessary. To solve that problem is not able to be approached with trial and error, or experiences of the experts. Therefore, more scientific solution has to be proposed thus DFNN is more user friendly, Genetic Algorithm overcomes that problems. Nonlinear system identification is a common testing of Fuzzy Neural Network to verify whether FNN might achieve the requirement or not. The Experiments show that Genetic Dynamic Fuzzy Neural Network Genetic (GDFNN) exhibits the best result which is compared with other methods.

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

http://hdl.handle.net/10453/29301