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Conference Paper: Chemical reaction optimization for the fuzzy rule learning problem

TitleChemical reaction optimization for the fuzzy rule learning problem
Authors
KeywordsChemical reaction optimization
Function modeling
Fuzzy rule learning problem
Mamdani fuzzy rule-based system
Metaheuristic
Power grid line estimation
Smart grid
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284
Citation
The 2012 IEEE Congress on Evolutionary Computation (CEC 2012), Brisbane, Australia, 10-15 June 2012. In IEEE CEC Proceedings, 2012, p. 1-8 How to Cite?
AbstractIn this paper, we utilize Chemical Reaction Optimization (CRO), a newly proposed metaheuristic for global optimization, to design Fuzzy Rule-Based Systems (FRBSs). CRO imitates the interactions of molecules in a chemical reaction. The molecular structure corresponds to a solution, and the potential energy is analogous to the objective function value. Molecules are driven toward the lowest energy stable state, which corresponds to the global optimum of the problem. In the realm of modeling with fuzzy rule-based systems, automatic derivation of fuzzy rules from numerical data plays a critical role. We propose to use CRO with Cooperative Rules (COR) to solve the fuzzy rule learning problem in FRBS. We formulate the learning process of FRBS in the form of a combinatorial optimization problem. Our proposed method COR-CRO is evaluated by two fuzzy modeling benchmarks and compared with other learning algorithms. Simulation results demonstrate that COR-CRO is highly competitive and outperforms many other existing optimization methods. © 2012 IEEE.
DescriptionIEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)
Persistent Identifierhttp://hdl.handle.net/10722/165304
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLam, AYSen_US
dc.contributor.authorLi, VOKen_US
dc.contributor.authorWei, Zen_US
dc.date.accessioned2012-09-20T08:16:51Z-
dc.date.available2012-09-20T08:16:51Z-
dc.date.issued2012en_US
dc.identifier.citationThe 2012 IEEE Congress on Evolutionary Computation (CEC 2012), Brisbane, Australia, 10-15 June 2012. In IEEE CEC Proceedings, 2012, p. 1-8en_US
dc.identifier.isbn978-1-4673-1509-8-
dc.identifier.urihttp://hdl.handle.net/10722/165304-
dc.descriptionIEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)-
dc.description.abstractIn this paper, we utilize Chemical Reaction Optimization (CRO), a newly proposed metaheuristic for global optimization, to design Fuzzy Rule-Based Systems (FRBSs). CRO imitates the interactions of molecules in a chemical reaction. The molecular structure corresponds to a solution, and the potential energy is analogous to the objective function value. Molecules are driven toward the lowest energy stable state, which corresponds to the global optimum of the problem. In the realm of modeling with fuzzy rule-based systems, automatic derivation of fuzzy rules from numerical data plays a critical role. We propose to use CRO with Cooperative Rules (COR) to solve the fuzzy rule learning problem in FRBS. We formulate the learning process of FRBS in the form of a combinatorial optimization problem. Our proposed method COR-CRO is evaluated by two fuzzy modeling benchmarks and compared with other learning algorithms. Simulation results demonstrate that COR-CRO is highly competitive and outperforms many other existing optimization methods. © 2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284-
dc.relation.ispartofIEEE Congress on Evolutionary Computationen_US
dc.rightsIEEE Congress on Evolutionary Computation. Copyright © IEEE.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectChemical reaction optimization-
dc.subjectFunction modeling-
dc.subjectFuzzy rule learning problem-
dc.subjectMamdani fuzzy rule-based system-
dc.subjectMetaheuristic-
dc.subjectPower grid line estimation-
dc.subjectSmart grid-
dc.titleChemical reaction optimization for the fuzzy rule learning problemen_US
dc.typeConference_Paperen_US
dc.identifier.emailLam, AYS: ayslam@eee.hku.hken_US
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLi, VOK=rp00150en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CEC.2012.6256570-
dc.identifier.scopuseid_2-s2.0-84866856763-
dc.identifier.hkuros210465en_US
dc.identifier.hkuros261765-
dc.identifier.spage1-
dc.identifier.epage8-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 130508 ; sml 160909 - merged-

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