Design of Rule-Based Neurofuzzy Networks by Means of Genetic Fuzzy Set-Based Granulation
In this paper, new architectures and design methodologies of Rule based Neurofuzzy Networks (RNFN) are introduced and the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. The proposed RNFN is based on the fuzzy set based neu...
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| creator | Park, Byoungjun Oh, Sungkwun |
| description | In this paper, new architectures and design methodologies of Rule based Neurofuzzy Networks (RNFN) are introduced and the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. The proposed RNFN is based on the fuzzy set based neurofuzzy networks (NFN) with the extended structure of fuzzy rules being formed within the networks. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and modified quadratic are taken into consideration. The dynamic search-based GAs optimizes the structure and parameters of the RNFN. |
| doi_str_mv | 10.1007/11427391_67 |
| format | Conference Proceeding |
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The proposed RNFN is based on the fuzzy set based neurofuzzy networks (NFN) with the extended structure of fuzzy rules being formed within the networks. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and modified quadratic are taken into consideration. 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The dynamic search-based GAs optimizes the structure and parameters of the RNFN.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Consequence Part</subject><subject>Exact sciences and technology</subject><subject>Fuzzy Modeling</subject><subject>Fuzzy Neural Network</subject><subject>Fuzzy Rule</subject><subject>Learning and adaptive systems</subject><subject>Optimal Convergence</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540259121</isbn><isbn>3540259120</isbn><isbn>9783540320654</isbn><isbn>3540320652</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkMtOwzAQRc1Loipd8QPZsGAR8PjtJbS0IBWQeEjsLMexq9CQVHEi1H49Ka0Qs5nR3DNXo4vQOeArwFheAzAiqQYj5AEaaakoZ5gSLDg7RAMQACmlTB_9aYRrIHCMBphikmrJ6CkaxfiJ-6Ig-u0AfUx8LBZVUofkpSt9emujz5Mn3zV16DabdT-233WzjEm2Th69reIWnfnKt4VLpr_Iq2_3d7PGVl1p26KuztBJsGX0o30fovfp3dv4Pp0_zx7GN_N0RUC3qaKEex60Doo7KnnOVOaloIJJFSy3eZ5J7IVjhGjCA6E8E8yyAMGBV87RIbrY-a5sdLYM_QeuiGbVFF-2WRsQShEF0HOXOy72UrXwjcnqehkNYLNN1_xLl_4Ab45mGw</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Park, Byoungjun</creator><creator>Oh, Sungkwun</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>Design of Rule-Based Neurofuzzy Networks by Means of Genetic Fuzzy Set-Based Granulation</title><author>Park, Byoungjun ; Oh, Sungkwun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-8325e5f99f85c375d48be7636478fa5addb70e6c422925f235b64a4f1fc1e8cc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Consequence Part</topic><topic>Exact sciences and technology</topic><topic>Fuzzy Modeling</topic><topic>Fuzzy Neural Network</topic><topic>Fuzzy Rule</topic><topic>Learning and adaptive systems</topic><topic>Optimal Convergence</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Park, Byoungjun</creatorcontrib><creatorcontrib>Oh, Sungkwun</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Park, Byoungjun</au><au>Oh, Sungkwun</au><au>Yi, Zhang</au><au>Liao, Xiaofeng</au><au>Wang, Jun</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Design of Rule-Based Neurofuzzy Networks by Means of Genetic Fuzzy Set-Based Granulation</atitle><btitle>Advances in Neural Networks – ISNN 2005</btitle><date>2005</date><risdate>2005</risdate><spage>422</spage><epage>427</epage><pages>422-427</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540259121</isbn><isbn>3540259120</isbn><eisbn>9783540320654</eisbn><eisbn>3540320652</eisbn><abstract>In this paper, new architectures and design methodologies of Rule based Neurofuzzy Networks (RNFN) are introduced and the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. 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| fulltext | fulltext |
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| ispartof | Advances in Neural Networks – ISNN 2005, 2005, p.422-427 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16882811 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Consequence Part Exact sciences and technology Fuzzy Modeling Fuzzy Neural Network Fuzzy Rule Learning and adaptive systems Optimal Convergence |
| title | Design of Rule-Based Neurofuzzy Networks by Means of Genetic Fuzzy Set-Based Granulation |
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