Design and tuning of fuzzy if-then rules for automatic classification
We propose an approach to the design and tuning of fuzzy rules for automatic classification decision making. This approach is based upon the finding of the weights of fuzzy if-then rules and the shapes of membership functions that minimize the difference between real (desired) and inferred (theoreti...
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creator | Rotshtein, A. Katelnikov, D. |
description | We propose an approach to the design and tuning of fuzzy rules for automatic classification decision making. This approach is based upon the finding of the weights of fuzzy if-then rules and the shapes of membership functions that minimize the difference between real (desired) and inferred (theoretical) classes of decisions. The problem of fuzzy model tuning is stated as a classical mathematical optimization problem. |
doi_str_mv | 10.1109/NAFIPS.1998.715528 |
format | Conference Proceeding |
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The problem of fuzzy model tuning is stated as a classical mathematical optimization problem.</description><subject>Automatic control</subject><subject>Control systems</subject><subject>Decision making</subject><subject>Fuzzy logic</subject><subject>Fuzzy systems</subject><subject>Information management</subject><subject>Input variables</subject><subject>Medical control systems</subject><subject>Medical diagnostic imaging</subject><subject>Shape</subject><isbn>9780780344532</isbn><isbn>0780344537</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1998</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj1FLwzAUhQMiKLN_YE_5A61NckuSxzE3HQwV1Odxm97MSJdKkz5sv97CPBw4fA_nwGFsKepKiNo-vq62u_ePSlhrKi2aRpobVlht6tkKoFHyjhUp_dSzlAUL8p5tniiFY-QYO56nGOKRD5776XI58-DL_E2Rj1NPifth5Djl4YQ5OO56TCn44GYa4gO79dgnKv5zwb62m8_1S7l_e96tV_syCA25NKDarjO6a5GIgABIkPGulRadlkaRMmSlQ9NKJQGFI-nIekQhnKFOLdjyuhvm_uF3DCccz4frV_UH3kZMPw</recordid><startdate>1998</startdate><enddate>1998</enddate><creator>Rotshtein, A.</creator><creator>Katelnikov, D.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1998</creationdate><title>Design and tuning of fuzzy if-then rules for automatic classification</title><author>Rotshtein, A. ; Katelnikov, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i174t-843bdd87dbaeee4e44e1e8fcb29ac7283e38e92ca8b2324a1ce2ce9faa11c8ed3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Automatic control</topic><topic>Control systems</topic><topic>Decision making</topic><topic>Fuzzy logic</topic><topic>Fuzzy systems</topic><topic>Information management</topic><topic>Input variables</topic><topic>Medical control systems</topic><topic>Medical diagnostic imaging</topic><topic>Shape</topic><toplevel>online_resources</toplevel><creatorcontrib>Rotshtein, A.</creatorcontrib><creatorcontrib>Katelnikov, D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Rotshtein, A.</au><au>Katelnikov, D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Design and tuning of fuzzy if-then rules for automatic classification</atitle><btitle>1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.98TH8353)</btitle><stitle>NAFIPS</stitle><date>1998</date><risdate>1998</risdate><spage>50</spage><epage>54</epage><pages>50-54</pages><isbn>9780780344532</isbn><isbn>0780344537</isbn><abstract>We propose an approach to the design and tuning of fuzzy rules for automatic classification decision making. This approach is based upon the finding of the weights of fuzzy if-then rules and the shapes of membership functions that minimize the difference between real (desired) and inferred (theoretical) classes of decisions. The problem of fuzzy model tuning is stated as a classical mathematical optimization problem.</abstract><pub>IEEE</pub><doi>10.1109/NAFIPS.1998.715528</doi><tpages>5</tpages></addata></record> |
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identifier | ISBN: 9780780344532 |
ispartof | 1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.98TH8353), 1998, p.50-54 |
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language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Automatic control Control systems Decision making Fuzzy logic Fuzzy systems Information management Input variables Medical control systems Medical diagnostic imaging Shape |
title | Design and tuning of fuzzy if-then rules for automatic classification |
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