A self-organization mining based hybrid evolution learning for TSK-type fuzzy model design
In this paper, a self-organization mining based hybrid evolution (SOME) learning algorithm for designing a TSK-type fuzzy model (TFM) is proposed. In the proposed SOME, group-based symbiotic evolution (GSE) is adopted in which each group in the GSE represents a collection of only one fuzzy rule. The...
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| Vydáno v: | Applied intelligence (Dordrecht, Netherlands) Ročník 36; číslo 2; s. 454 - 471 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.03.2012
Springer Nature B.V |
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| ISSN: | 0924-669X, 1573-7497 |
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| Abstract | In this paper, a self-organization mining based hybrid evolution (SOME) learning algorithm for designing a TSK-type fuzzy model (TFM) is proposed. In the proposed SOME, group-based symbiotic evolution (GSE) is adopted in which each group in the GSE represents a collection of only one fuzzy rule. The proposed SOME consists of structure learning and parameter learning. In structure learning, the proposed SOME uses a two-step self-organization algorithm to decide the suitable number of rules in a TFM. In parameter learning, the proposed SOME uses the data mining based selection strategy and data mining based crossover strategy to decide groups and parental groups by the data mining algorithm that called frequent pattern growth. Illustrative examples were conducted to verify the performance and applicability of the proposed SOME method. |
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| AbstractList | In this paper, a self-organization mining based hybrid evolution (SOME) learning algorithm for designing a TSK-type fuzzy model (TFM) is proposed. In the proposed SOME, group-based symbiotic evolution (GSE) is adopted in which each group in the GSE represents a collection of only one fuzzy rule. The proposed SOME consists of structure learning and parameter learning. In structure learning, the proposed SOME uses a two-step self-organization algorithm to decide the suitable number of rules in a TFM. In parameter learning, the proposed SOME uses the data mining based selection strategy and data mining based crossover strategy to decide groups and parental groups by the data mining algorithm that called frequent pattern growth. Illustrative examples were conducted to verify the performance and applicability of the proposed SOME method. In this paper, a self-organization mining based hybrid evolution (SOME) learning algorithm for designing a TSK-type fuzzy model (TFM) is proposed. In the proposed SOME, group-based symbiotic evolution (GSE) is adopted in which each group in the GSE represents a collection of only one fuzzy rule. The proposed SOME consists of structure learning and parameter learning. In structure learning, the proposed SOME uses a two-step self-organization algorithm to decide the suitable number of rules in a TFM. In parameter learning, the proposed SOME uses the data mining based selection strategy and data mining based crossover strategy to decide groups and parental groups by the data mining algorithm that called frequent pattern growth. Illustrative examples were conducted to verify the performance and applicability of the proposed SOME method.[PUBLICATION ABSTRACT] |
| Author | Lin, Sheng-Fuu Chang, Jyun-Wei Hsu, Yung-Chi |
| Author_xml | – sequence: 1 givenname: Sheng-Fuu surname: Lin fullname: Lin, Sheng-Fuu email: sflin@mail.nctu.edu.tw organization: Department of Electrical Engineering, National Chiao Tung University – sequence: 2 givenname: Jyun-Wei surname: Chang fullname: Chang, Jyun-Wei organization: Department of Electrical Engineering, National Chiao Tung University – sequence: 3 givenname: Yung-Chi surname: Hsu fullname: Hsu, Yung-Chi organization: Graduate Institute of Network Learning Technology, National Central University |
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| Cites_doi | 10.1080/03081070500132377 10.1016/0165-0114(95)00196-4 10.1109/ICEC.1994.350039 10.1142/4177 10.1109/91.649913 10.1109/TFUZZ.2006.889920 10.1109/91.705501 10.1109/91.995118 10.1109/3477.836377 10.1109/41.170970 10.1002/0471687545 10.1109/4235.873236 10.1002/acs.882 10.1016/j.mcm.2005.08.008 10.1109/3477.891151 10.1109/TSMC.1985.6313399 10.1145/37402.37406 10.1109/91.388168 10.1023/A:1021986309149 10.1109/TFUZZ.1993.390281 10.1007/s10489-006-6925-0 10.1162/evco.1993.1.2.127 10.1016/S1088-467X(99)00028-1 10.1007/s10489-007-0107-6 10.1145/335191.335372 10.1109/FUZZY.1993.327418 10.1109/TPWRS.2004.825924 10.1016/j.fss.2005.09.001 10.1109/91.580797 |
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| Keywords | Identification Data mining Genetic algorithm Fuzzy model FP-Growth Group-based symbiotic evolution |
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| References_xml | – reference: LinCJXuYJThe design of tsk-type fuzzy controllers using a new hybrid learning approachInt J Adapt Control Signal Process200620112521991281085.9351610.1002/acs.882 – reference: AgrawalRSrikantRFast algorithms for mining association rules in large databasesProceedings of the 20th international conference on very large data bases1994487499 – reference: ArabasJMichalewiczZMulawkaJGAVAPS-a genetic algorithm with varying population sizeProceedings of the IEEE world congress on computational intelligence1994737810.1109/ICEC.1994.350039 – reference: GoldbergDEGenetic algorithms in search, optimization, and machine learning1989ReadingAddison-Wesley0721.68056 – reference: FogelLJZuradaJMMarksJMGoldbergCEvolutionary programming in perspective: The top-down viewComputational intelligence: imitating life1994New YorkIEEE Press – reference: SmithREForrestSPerelsonASSearching for diverse, cooperative populations with genetic algorithmsEvol Comput19931212714910.1162/evco.1993.1.2.127 – reference: LinYHCunninghamGACoggeshallSVUsing fuzzy partitions to create fuzzy systems from input-output data and set the initial weights in a fuzzy neural networkIEEE Trans Fuzzy Syst19975461462110.1109/91.649913 – reference: LinCJHsuYCReinforcement hybrid evolutionary learning for recurrent wavelet-based neurofuzzy systemsIEEE Trans Fuzzy Syst200715472974510.1109/TFUZZ.2006.889920 – reference: ReynoldsCWFlocks, herds and schools: a distributed behavioral modelComput Graph1987214253492453810.1145/37402.37406 – reference: KarrCLDesign of an adaptive fuzzy logic controller using a genetic algorithmProceedings of the 4th international conference on genetic algorithms1991450457 – reference: JuangCFLinJYLinCTGenetic reinforcement learning through symbiotic evolution for fuzzy controller designIEEE Trans Syst Man Cybern, Part B, Cybern2000302290302174136310.1109/3477.836377 – reference: JangJ-SRSunC-TMizutaniENeuro-fuzzy and soft computing: a 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| SubjectTerms | Algorithms Artificial Intelligence Chromosomes Computer Science Controllers Data mining Evolution Fuzzy Fuzzy logic Fuzzy set theory Fuzzy sets Genetic algorithms Learning Machines Manufacturing Mechanical Engineering Mutation Network topologies Optimization techniques Processes Strategy |
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| Title | A self-organization mining based hybrid evolution learning for TSK-type fuzzy model design |
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