A TSK Fuzzy Inference Algorithm for Online Identification
This paper proposes an online self-organizing identification algorithm for TSK fuzzy model. The structure of TSK fuzzy model is identified using distance. Parameters of the piecewise linear function consisting consequent part are obtained using recursive version of combined learning method of global...
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| creator | Kim, Kyoungjung Whang, Eun Ju Park, Chang-Woo Kim, Euntai Park, Mignon |
| description | This paper proposes an online self-organizing identification algorithm for TSK fuzzy model. The structure of TSK fuzzy model is identified using distance. Parameters of the piecewise linear function consisting consequent part are obtained using recursive version of combined learning method of global and local learning. Both input and output spaces are considered in the proposed algorithm to identify the structure of the TSK fuzzy model. By processing clustering both in input and output space, outliers are excluded in clustering effectively. The proposed algorithm is non-sensitive to noise not by using data itself as cluster centers. The proposed algorithm can obtain a TSK fuzzy model through one pass. By using the proposed combined learning method, the estimated function can have high accuracy. |
| doi_str_mv | 10.1007/11539506_23 |
| format | Book Chapter |
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The structure of TSK fuzzy model is identified using distance. Parameters of the piecewise linear function consisting consequent part are obtained using recursive version of combined learning method of global and local learning. Both input and output spaces are considered in the proposed algorithm to identify the structure of the TSK fuzzy model. By processing clustering both in input and output space, outliers are excluded in clustering effectively. The proposed algorithm is non-sensitive to noise not by using data itself as cluster centers. The proposed algorithm can obtain a TSK fuzzy model through one pass. By using the proposed combined learning method, the estimated function can have high accuracy.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540283129</identifier><identifier>ISBN: 9783540283126</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540318305</identifier><identifier>EISBN: 9783540318309</identifier><identifier>DOI: 10.1007/11539506_23</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Cluster Center ; Fuzzy Neural Network ; Fuzzy Rule ; Fuzzy System ; Input Space</subject><ispartof>Fuzzy Systems and Knowledge Discovery, 2005, p.179-188</ispartof><rights>Springer-Verlag Berlin Heidelberg 2005</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><relation>Lecture Notes in Computer Science</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11539506_23$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11539506_23$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>777,778,782,791,27908,38238,41425,42494</link.rule.ids></links><search><contributor>Jin, Yaochu</contributor><contributor>Wang, Lipo</contributor><creatorcontrib>Kim, Kyoungjung</creatorcontrib><creatorcontrib>Whang, Eun Ju</creatorcontrib><creatorcontrib>Park, Chang-Woo</creatorcontrib><creatorcontrib>Kim, Euntai</creatorcontrib><creatorcontrib>Park, Mignon</creatorcontrib><title>A TSK Fuzzy Inference Algorithm for Online Identification</title><title>Fuzzy Systems and Knowledge Discovery</title><description>This paper proposes an online self-organizing identification algorithm for TSK fuzzy model. The structure of TSK fuzzy model is identified using distance. Parameters of the piecewise linear function consisting consequent part are obtained using recursive version of combined learning method of global and local learning. Both input and output spaces are considered in the proposed algorithm to identify the structure of the TSK fuzzy model. By processing clustering both in input and output space, outliers are excluded in clustering effectively. The proposed algorithm is non-sensitive to noise not by using data itself as cluster centers. The proposed algorithm can obtain a TSK fuzzy model through one pass. 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The structure of TSK fuzzy model is identified using distance. Parameters of the piecewise linear function consisting consequent part are obtained using recursive version of combined learning method of global and local learning. Both input and output spaces are considered in the proposed algorithm to identify the structure of the TSK fuzzy model. By processing clustering both in input and output space, outliers are excluded in clustering effectively. The proposed algorithm is non-sensitive to noise not by using data itself as cluster centers. The proposed algorithm can obtain a TSK fuzzy model through one pass. By using the proposed combined learning method, the estimated function can have high accuracy.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11539506_23</doi><tpages>10</tpages></addata></record> |
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| ispartof | Fuzzy Systems and Knowledge Discovery, 2005, p.179-188 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_springer_books_10_1007_11539506_23 |
| source | Springer Books |
| subjects | Cluster Center Fuzzy Neural Network Fuzzy Rule Fuzzy System Input Space |
| title | A TSK Fuzzy Inference Algorithm for Online Identification |
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