Design of interval type-2 fuzzy neural networks and their optimization using real-coded genetic algorithms
In this paper, we introduce the design methodology of interval type-2 fuzzy neural networks (IT2FNN). And to optimize the network we use a real-coded genetic algorithm. IT2FNN is the network of combination between the fuzzy neural network (FNN) and interval type-2 fuzzy set with uncertainty. The ant...
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| creator | Keon-Jun Park Sung-Kwun Oh Pedrycz, W. |
| description | In this paper, we introduce the design methodology of interval type-2 fuzzy neural networks (IT2FNN). And to optimize the network we use a real-coded genetic algorithm. IT2FNN is the network of combination between the fuzzy neural network (FNN) and interval type-2 fuzzy set with uncertainty. The antecedent part of the network is composed of the fuzzy division of input space and the consequence part of the network is represented by polynomial functions. The parameters such as the apexes of membership function, uncertainty parameter, the learning rate and the momentum coefficient are optimized using genetic algorithm (GA). The proposed network is evaluated with the performance between the approximation and the generalization abilities. |
| doi_str_mv | 10.1109/FUZZY.2009.5277365 |
| format | Conference Proceeding |
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The proposed network is evaluated with the performance between the approximation and the generalization abilities.</description><subject>Algorithm design and analysis</subject><subject>Design optimization</subject><subject>Fuzzy neural networks</subject><subject>Fuzzy sets</subject><subject>Genetic algorithms</subject><subject>Inference algorithms</subject><subject>Neural networks</subject><subject>Polynomials</subject><subject>Uncertainty</subject><subject>Working environment noise</subject><issn>1098-7584</issn><isbn>9781424435968</isbn><isbn>142443596X</isbn><isbn>9781424435975</isbn><isbn>1424435978</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVUE1PAjEUrFETCfIH9NI_sNhP2h4NipqQeJGDXMhz93UpLl3SLRr49a6Ri3OZvElmMm8IueFszDlzd7PFcvk-Foy5sRbGyIk-IyNnLFdCKamd0ef_7om9IIPeaAujrboio67bsB5KSy75gGwesAt1pK2nIWZMX9DQfNhhIajfH48HGnGfei1i_m7TZ0chVjSvMSTa7nLYhiPk0Ea670KsaUJoirKtsKI19pZQUmjqNoW83nbX5NJD0-HoxEOymD2-TZ-L-evTy_R-XgRudC6c-i3nuSuZ5ApsqSUzDjhYEJWySlTee8WY7R8Ay8oPRA0VBw1CaK9RDsntX25AxNUuhS2kw-q0lvwBn1pdaA</recordid><startdate>200908</startdate><enddate>200908</enddate><creator>Keon-Jun Park</creator><creator>Sung-Kwun Oh</creator><creator>Pedrycz, W.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200908</creationdate><title>Design of interval type-2 fuzzy neural networks and their optimization using real-coded genetic algorithms</title><author>Keon-Jun Park ; Sung-Kwun Oh ; Pedrycz, W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-940453f19c0314a8c53079a1a8a2d4842dfff4008313a80cbee5ad1a5a225f5e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Algorithm design and analysis</topic><topic>Design optimization</topic><topic>Fuzzy neural networks</topic><topic>Fuzzy sets</topic><topic>Genetic algorithms</topic><topic>Inference algorithms</topic><topic>Neural networks</topic><topic>Polynomials</topic><topic>Uncertainty</topic><topic>Working environment noise</topic><toplevel>online_resources</toplevel><creatorcontrib>Keon-Jun Park</creatorcontrib><creatorcontrib>Sung-Kwun Oh</creatorcontrib><creatorcontrib>Pedrycz, W.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Keon-Jun Park</au><au>Sung-Kwun Oh</au><au>Pedrycz, W.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Design of interval type-2 fuzzy neural networks and their optimization using real-coded genetic algorithms</atitle><btitle>2009 IEEE International Conference on Fuzzy Systems</btitle><stitle>FUZZY</stitle><date>2009-08</date><risdate>2009</risdate><spage>2013</spage><epage>2018</epage><pages>2013-2018</pages><issn>1098-7584</issn><isbn>9781424435968</isbn><isbn>142443596X</isbn><eisbn>9781424435975</eisbn><eisbn>1424435978</eisbn><abstract>In this paper, we introduce the design methodology of interval type-2 fuzzy neural networks (IT2FNN). And to optimize the network we use a real-coded genetic algorithm. IT2FNN is the network of combination between the fuzzy neural network (FNN) and interval type-2 fuzzy set with uncertainty. The antecedent part of the network is composed of the fuzzy division of input space and the consequence part of the network is represented by polynomial functions. The parameters such as the apexes of membership function, uncertainty parameter, the learning rate and the momentum coefficient are optimized using genetic algorithm (GA). The proposed network is evaluated with the performance between the approximation and the generalization abilities.</abstract><pub>IEEE</pub><doi>10.1109/FUZZY.2009.5277365</doi><tpages>6</tpages></addata></record> |
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| language | eng |
| recordid | cdi_ieee_primary_5277365 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Design optimization Fuzzy neural networks Fuzzy sets Genetic algorithms Inference algorithms Neural networks Polynomials Uncertainty Working environment noise |
| title | Design of interval type-2 fuzzy neural networks and their optimization using real-coded genetic algorithms |
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