A new approach to genetics based machine learning in fuzzy controller design
This paper proposes an evolutionary approach to fuzzy controller design based on the "Pittsburgh" style classifier system in which whole rule-sets are the unit of credit assignment and selection. We present a description of a system based on this idea, together with experimental results us...
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| creator | Carse, B. Fogarty, T.C. |
| description | This paper proposes an evolutionary approach to fuzzy controller design based on the "Pittsburgh" style classifier system in which whole rule-sets are the unit of credit assignment and selection. We present a description of a system based on this idea, together with experimental results using the system to learn function identification. The representation used allows the genetic algorithm to vary both membership functions (centres and widths) and fuzzy relations. We introduce a new crossover operator which employs crosspoints in the input space and demonstrate its efficacy. Finally, we present results which show that the classifier system is capable of self-organisation of membership functions and fuzzy relations simultaneously.< > |
| doi_str_mv | 10.1109/ISIC.1994.367812 |
| format | Conference Proceeding |
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Finally, we present results which show that the classifier system is capable of self-organisation of membership functions and fuzzy relations simultaneously.< ></description><subject>Automatic control</subject><subject>Environmental economics</subject><subject>Fuzzy control</subject><subject>Fuzzy set theory</subject><subject>Fuzzy sets</subject><subject>Fuzzy systems</subject><subject>Genetics</subject><subject>Machine learning</subject><subject>Motion control</subject><subject>Temperature control</subject><issn>2158-9860</issn><issn>2158-9879</issn><isbn>9780780319905</isbn><isbn>0780319907</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1994</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9UE1LxDAUDH6Ay9q7eMofaE2aNMk7LsWPQsGDel7S9KVGumlpK7L76y2sOAwMzDwezBByx1nGOYOH6q0qMw4gM6G04fkF2eS8MCkYDZckAW3YSrFesOLqP1PshiTz_MVWyIKB4htS72jEH2rHcRqs-6TLQDuMuAQ308bO2NLDaoeItEc7xRA7GiL136fTkbohLtPQ9zjRFufQxVty7W0_Y_KnW_Lx9PhevqT163NV7uo0cCaXFFgjdK4Mb41CafO2kR6w9QwlNE4IITXwQhfopVGeAcN87WGkMwq8a6zYkvvz34CI-3EKBzsd9-cpxC9Hg0_Z</recordid><startdate>1994</startdate><enddate>1994</enddate><creator>Carse, B.</creator><creator>Fogarty, T.C.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1994</creationdate><title>A new approach to genetics based machine learning in fuzzy controller design</title><author>Carse, B. ; Fogarty, T.C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-90b372681d86e4a2db4f9edf0e49bc3334791575ef486f090e290584c869fcba3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1994</creationdate><topic>Automatic control</topic><topic>Environmental economics</topic><topic>Fuzzy control</topic><topic>Fuzzy set theory</topic><topic>Fuzzy sets</topic><topic>Fuzzy systems</topic><topic>Genetics</topic><topic>Machine learning</topic><topic>Motion control</topic><topic>Temperature control</topic><toplevel>online_resources</toplevel><creatorcontrib>Carse, B.</creatorcontrib><creatorcontrib>Fogarty, T.C.</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/IET Electronic Library</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>Carse, B.</au><au>Fogarty, T.C.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A new approach to genetics based machine learning in fuzzy controller design</atitle><btitle>Proceedings of 1994 9th IEEE International Symposium on Intelligent Control</btitle><stitle>ISIC</stitle><date>1994</date><risdate>1994</risdate><spage>231</spage><epage>236</epage><pages>231-236</pages><issn>2158-9860</issn><eissn>2158-9879</eissn><isbn>9780780319905</isbn><isbn>0780319907</isbn><abstract>This paper proposes an evolutionary approach to fuzzy controller design based on the "Pittsburgh" style classifier system in which whole rule-sets are the unit of credit assignment and selection. We present a description of a system based on this idea, together with experimental results using the system to learn function identification. The representation used allows the genetic algorithm to vary both membership functions (centres and widths) and fuzzy relations. We introduce a new crossover operator which employs crosspoints in the input space and demonstrate its efficacy. Finally, we present results which show that the classifier system is capable of self-organisation of membership functions and fuzzy relations simultaneously.< ></abstract><pub>IEEE</pub><doi>10.1109/ISIC.1994.367812</doi><tpages>6</tpages></addata></record> |
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| identifier | ISSN: 2158-9860 |
| ispartof | Proceedings of 1994 9th IEEE International Symposium on Intelligent Control, 1994, p.231-236 |
| issn | 2158-9860 2158-9879 |
| language | eng |
| recordid | cdi_ieee_primary_367812 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Automatic control Environmental economics Fuzzy control Fuzzy set theory Fuzzy sets Fuzzy systems Genetics Machine learning Motion control Temperature control |
| title | A new approach to genetics based machine learning in fuzzy controller design |
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