A multi-objective subtractive FCM based TSK fuzzy system with input selection, and its application to dynamic inverse modelling of MR dampers

Askari, M; Li, J; Samali, B

A multi-objective subtractive FCM based TSK fuzzy system with input selection, and its application to dynamic inverse modelling of MR dampers

Askari, M

Li, J

Samali, B

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7894 LNAI (PART 1), pp. 215 - 226
Issue Date:: 2013-09-25

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Field	Value	Language
dc.contributor.author	Askari, M https://orcid.org/0000-0002-0239-4651	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.contributor.author	Samali, B https://orcid.org/0000-0002-3426-7745	en_US
dc.date.issued	2013-09-25	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7894 LNAI (PART 1), pp. 215 - 226	en_US
dc.identifier.isbn	9783642386572	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28401
dc.identifier.uri	http://hdl.handle.net/10453/32368
dc.description.abstract	A new encoding scheme is presented for a fuzzy-based nonlinear system identification methodology, using the subtractive Fuzzy C-Mean clustering and a modified version of non-dominated sorting genetic algorithm. This method is able to automatically select the best inputs as well as the structure of the fuzzy model such as rules and membership functions. Moreover, three objective functions are considered to satisfy both accuracy and compactness of the model. The proposed method is then employed to identify the inverse model of a highly nonlinear structural control device, namely Magnetorheological (MR) damper. It is shown that the developed evolving Takagi-Sugeno-Kang (TSK) fuzzy model can identify and grasp the nonlinear dynamics of inverse systems very well, while a small number of inputs and fuzzy rules are required for this purpose. © 2013 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-38658-9_20	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A multi-objective subtractive FCM based TSK fuzzy system with input selection, and its application to dynamic inverse modelling of MR dampers	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 1	en_US
utslib.citation.volume	7894 LNAI	en_US
utslib.for	0905 Civil Engineering	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 Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.issue	PART 1	en_US
pubs.publication-status	Published	en_US
pubs.volume	7894 LNAI	en_US

Abstract:

A new encoding scheme is presented for a fuzzy-based nonlinear system identification methodology, using the subtractive Fuzzy C-Mean clustering and a modified version of non-dominated sorting genetic algorithm. This method is able to automatically select the best inputs as well as the structure of the fuzzy model such as rules and membership functions. Moreover, three objective functions are considered to satisfy both accuracy and compactness of the model. The proposed method is then employed to identify the inverse model of a highly nonlinear structural control device, namely Magnetorheological (MR) damper. It is shown that the developed evolving Takagi-Sugeno-Kang (TSK) fuzzy model can identify and grasp the nonlinear dynamics of inverse systems very well, while a small number of inputs and fuzzy rules are required for this purpose. © 2013 Springer-Verlag.

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http://hdl.handle.net/10453/28401