Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/78261
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHuang, L.-
dc.contributor.authorWang, K.-
dc.contributor.authorShi, P.-
dc.contributor.authorKarimi, H.-
dc.date.issued2012-
dc.identifier.citationMathematical Problems in Engineering, 2012; 2012:1-12-
dc.identifier.issn1024-123X-
dc.identifier.issn1563-5147-
dc.identifier.urihttp://hdl.handle.net/2440/78261-
dc.descriptionExtent: 12p.-
dc.description.abstractIn order to approximate any nonlinear system, not just affine nonlinear systems, generalized T-S fuzzy systems, where the control variables and the state variables, are all premise variables are introduced in the paper. Firstly, fuzzy spaces and rules were determined by using ant colony algorithm. Secondly, the state-space model parameters are identified by using genetic algorithm. The simulation results show the effectiveness of the proposed algorithm.-
dc.description.statementofresponsibilityLing Huang, Kai Wang, Peng Shi and Hamid Reza Karimi-
dc.language.isoen-
dc.publisherGordon Breach Sci Publ Ltd-
dc.rightsCopyright © 2012 Ling Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.-
dc.source.urihttp://dx.doi.org/10.1155/2012/893807-
dc.titleA novel identification method for generalized T-S fuzzy systems-
dc.typeJournal article-
dc.identifier.doi10.1155/2012/893807-
pubs.publication-statusPublished-
dc.identifier.orcidShi, P. [0000-0001-8218-586X]-
Appears in Collections:Aurora harvest 4
Electrical and Electronic Engineering publications

Files in This Item:
File Description SizeFormat 
hdl_78261.pdfPublished version1.56 MBAdobe PDFView/Open


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