A method for compiling neural networks into fuzzy rules using genetic algorithms and hierarchical approach
Neural networks have been criticized for their lack of human comprehensibility, which make them to appear as black box structures to the user. The paper proposes a mechanism that compiles a neural network into an equivalent set of fuzzy rules. Genetic algorithms are used to find the right structure...
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| container_end_page | 358 vol.2 |
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| creator | Palade, V. Bumbaru, S. Negoita, G. |
| description | Neural networks have been criticized for their lack of human comprehensibility, which make them to appear as black box structures to the user. The paper proposes a mechanism that compiles a neural network into an equivalent set of fuzzy rules. Genetic algorithms are used to find the right structure of the fuzzy model equivalent with the neural network, and then to find the best shape of the membership functions. In order to reduce the number of fuzzy rules, we look for a hierarchical structure of the fuzzy system, considering the relations between the network inputs. |
| doi_str_mv | 10.1109/KES.1998.725933 |
| format | Conference Proceeding |
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In order to reduce the number of fuzzy rules, we look for a hierarchical structure of the fuzzy system, considering the relations between the network inputs.</description><subject>Fuzzy control</subject><subject>Fuzzy logic</subject><subject>Fuzzy neural networks</subject><subject>Fuzzy sets</subject><subject>Fuzzy systems</subject><subject>Genetic algorithms</subject><subject>Hierarchical systems</subject><subject>Humans</subject><subject>Neural networks</subject><subject>Shape</subject><isbn>9780780343160</isbn><isbn>0780343166</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1998</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkMtqwzAURAWl0JJmXehKP-BUDyuSliGkDxroou06XOthK7UtI9mU5Ovrkg4DszmcxSB0T8mKUqIf33YfK6q1WkkmNOdXaKmlInN5yema3KBlzkcypxSCUnWLjhvcubGJFvuYsIndENrQ17h3U4J2nvEnpu-MQz9G7Kfz-YTT1LqMp_yH1W4mgsHQ1jGFsekyht7iJrgEyTTBzA4YhhTBNHfo2kOb3fJ_F-jrafe5fSn278-v282-CJSUY6EqbbW0TMu184wSQpUFJZkXQlIJ1khwTDEiLGhlGINKVmvmK04Fr6D0fIEeLt7gnDsMKXSQTofLIfwXj7ZYEg</recordid><startdate>1998</startdate><enddate>1998</enddate><creator>Palade, V.</creator><creator>Bumbaru, S.</creator><creator>Negoita, G.</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>A method for compiling neural networks into fuzzy rules using genetic algorithms and hierarchical approach</title><author>Palade, V. ; Bumbaru, S. ; Negoita, G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-8b9d97d2976ef210018da872f55717adc7ae28205da98c22ab7b62fb3153ba4f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Fuzzy control</topic><topic>Fuzzy logic</topic><topic>Fuzzy neural networks</topic><topic>Fuzzy sets</topic><topic>Fuzzy systems</topic><topic>Genetic algorithms</topic><topic>Hierarchical systems</topic><topic>Humans</topic><topic>Neural networks</topic><topic>Shape</topic><toplevel>online_resources</toplevel><creatorcontrib>Palade, V.</creatorcontrib><creatorcontrib>Bumbaru, S.</creatorcontrib><creatorcontrib>Negoita, G.</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>Palade, V.</au><au>Bumbaru, S.</au><au>Negoita, G.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A method for compiling neural networks into fuzzy rules using genetic algorithms and hierarchical approach</atitle><btitle>1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111)</btitle><stitle>KES</stitle><date>1998</date><risdate>1998</risdate><volume>2</volume><spage>353</spage><epage>358 vol.2</epage><pages>353-358 vol.2</pages><isbn>9780780343160</isbn><isbn>0780343166</isbn><abstract>Neural networks have been criticized for their lack of human comprehensibility, which make them to appear as black box structures to the user. The paper proposes a mechanism that compiles a neural network into an equivalent set of fuzzy rules. Genetic algorithms are used to find the right structure of the fuzzy model equivalent with the neural network, and then to find the best shape of the membership functions. In order to reduce the number of fuzzy rules, we look for a hierarchical structure of the fuzzy system, considering the relations between the network inputs.</abstract><pub>IEEE</pub><doi>10.1109/KES.1998.725933</doi></addata></record> |
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| ispartof | 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111), 1998, Vol.2, p.353-358 vol.2 |
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| language | eng |
| recordid | cdi_ieee_primary_725933 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Fuzzy control Fuzzy logic Fuzzy neural networks Fuzzy sets Fuzzy systems Genetic algorithms Hierarchical systems Humans Neural networks Shape |
| title | A method for compiling neural networks into fuzzy rules using genetic algorithms and hierarchical approach |
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