Data mining–based hierarchical cooperative coevolutionary algorithm for TSK-type neuro-fuzzy networks design
This study proposes a data mining–based hierarchical cooperative coevolutionary algorithm (DMHCCA) for TSK-type neuro-fuzzy networks design. The proposed DMHCCA consists of two-level evolutions: the neuro-level evolution (NULE) and the network-level evolution (NWLE). In NULE, a data mining–based evo...
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| Published in: | Neural computing & applications Vol. 23; no. 2; pp. 485 - 498 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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01.08.2013
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | This study proposes a data mining–based hierarchical cooperative coevolutionary algorithm (DMHCCA) for TSK-type neuro-fuzzy networks design. The proposed DMHCCA consists of two-level evolutions: the neuro-level evolution (NULE) and the network-level evolution (NWLE). In NULE, a data mining–based evolutionary learning algorithm is utilized to evolve neurons. The good combinations of neurons evolved in NULE are reserved for being the initial populations of NWLE. In NWLE, the initial population are mated and mutated to produce new structure of networks. Similar to NULE, the good neurons of evolved network in NWLE are inserted into the NULE. Thus, by interactive two-level evolutions, the neurons and structure of network can be evolved locally and globally, respectively. Simulation results using DMHCCA are reported and compared with other existing models. Application of DMHCCA to a three-dimensional (3D) surface alignment task is also described, and experimental results are presented better performance than other alignment systems. |
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| AbstractList | This study proposes a data mining–based hierarchical cooperative coevolutionary algorithm (DMHCCA) for TSK-type neuro-fuzzy networks design. The proposed DMHCCA consists of two-level evolutions: the neuro-level evolution (NULE) and the network-level evolution (NWLE). In NULE, a data mining–based evolutionary learning algorithm is utilized to evolve neurons. The good combinations of neurons evolved in NULE are reserved for being the initial populations of NWLE. In NWLE, the initial population are mated and mutated to produce new structure of networks. Similar to NULE, the good neurons of evolved network in NWLE are inserted into the NULE. Thus, by interactive two-level evolutions, the neurons and structure of network can be evolved locally and globally, respectively. Simulation results using DMHCCA are reported and compared with other existing models. Application of DMHCCA to a three-dimensional (3D) surface alignment task is also described, and experimental results are presented better performance than other alignment systems. |
| Author | Lin, Sheng-Fuu Chang, Jyun-Wei Hsu, Chi-Yao |
| Author_xml | – sequence: 1 givenname: Chi-Yao surname: Hsu fullname: Hsu, Chi-Yao organization: Department of Electrical Engineering, National Chiao Tung University – sequence: 2 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: 3 givenname: Jyun-Wei surname: Chang fullname: Chang, Jyun-Wei organization: Department of Electrical Engineering, National Chiao Tung University |
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| Keywords | Network-level evolution Data mining–based evolutionary learning algorithm Hierarchical cooperative coevolutionary algorithm Neuro-level evolution Three-dimensional surface alignment Network structure Neural computation Three-dimensional calculations Evolutionary algorithm Neural network Data mining Surface Alignment Experimental result Neuron Data mining―based evolutionary learning algorithm System performance Experimental design Learning algorithm |
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| References | LinCJLinCTAn ART-based fuzzy adaptive learning control networkIEEE Trans Fuzzy Syst19975447749610.1109/91.649900 LiuHYanJZhangDThree-dimensional surface registration: a neural network strategyNeurocomputing20067059760210.1016/j.neucom.2006.04.004 BeslPMckayNA method for registration of 3-D shapesIEEE Trans Pattern Anal Mach Intell199214223925610.1109/34.121791 Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceeding of ACM-SIGMOD, Dallas, Tx, pp 1–12 Cowder RS (1990) Predicting the mackey-glass time series with cascade-correlation learning. In: Proceedings of the 1990 connectionist models summer school, pp. 117–123 Agrawal R, Srikant R (1994) Fast algorithm for mining association rules. In: Proceeding of the international conference on VLDB, pp 487–499 TakagiHSuzukiNKodaTKojimaYNeural networks designed on approximated reasoning architecture and their applicationIEEE Trans Neural Netw19923575275910.1109/72.159063 CordonOHerreraFHoffmannFMagdalenaLGenetic fuzzy systems evolutionary tuning and learning of fuzzy knowledge bases, advances in fuzzy systems-applications and theory2001NJ, USAWorld Scientific Publishing10.1142/4177 Mizutani E, Jang J-SR (1995) Coactive neural fuzzy modeling. In: Proceeding of IEEE international conference on neural networks, pp 760–765 LinCJChenCHLinCTAn efficient evolutionary algorithm for fuzzy inference systemsEvol Syst2011228399 Gomez FJ (2003) Robust non-linear control through neuroevolution, Ph. D. Disseration, The University of Texas at Austin De Jong KA (1975) Analysis of the behavior of a class of genetic adaptive systems, Ph. D. Dissertation, Dep. Computer and Communication Sciences, Univ. Michigan, Ann Arbor, MI JuangCFLinCTAn on-line self-constructing neural fuzzy inference network and its applicationsIEEE Trans Fuzzy Syst199861123110.1109/91.660805 TanbeerSKAhmedCFJeongBSParallel and distributed algorithm for frequent pattern mining in large databaseIETE Tech Rev200926556510.4103/0256-4602.48469 LiuYHImproving ICP with easy implementation for free-form surface matchingPattern Recogn2004372112261059.6811110.1016/S0031-3203(03)00239-5 LinFJLinCHShenPHSelf-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor driveIEEE Trans Fuzzy Syst20019575175910.1109/91.963761 Wu YT, An YJ, Geller J, Wu YT (2006) A data mining based genetic algorithm. In: Proceeding of IEEE workshop SEUS-WCCIA, pp 27–28 JuangCFLinCTA recurrent self-organizing neural fuzzy inference networkIEEE Trans Neural Netw199910482884510.1109/72.774232 TakagiTSugenoMFuzzy identification of systems and its applications to modeling and controlIEEE Trans Syst Man Cybern1985151161320576.9302110.1109/TSMC.1985.6313399 LinSFChengYCTwo-strategy reinforcement evolutionary algorithm using data-mining based crossover strategy with TSK-type fuzzy controllersInt J Innovat Comput Control20106938633885 LiMWangZA hybrid coevolutionary algorithm for designing fuzzy classifiersInf Sci2009179121970198310.1016/j.ins.2009.01.045 ShankarSPurusothamanTUtility sentient frequent itemset mining and association rule mining: a literature survey and comparative studyInt J Soft Comput Appl200948195 CoxEFuzzy modeling and genetic algorithms for data mining and exploration20051San Francisco, USAMorgan Kaufman Publications1113.68072 ZhangJGeYOngSHChuiCKTeohSHYanCHRapid surface registration of 3D volumes using a neural network approachImage Vis Comput20082620121010.1016/j.imavis.2007.04.003 Gomez F, Schmidhuber J (2005) Co-evolving recurrent neurons learn deep memory POMDPs. In: Proceeding of conference on genetic and evolutionary computation, pp 491–498 LinCTJouCPGA-based fuzzy reinforcement learning for control of a magnetic bearing systemIEEE Trans Syst Man Cybern Part B2000302276289 LinC-JChinC-CPrediction and identification using wavelet-based recurrent fuzzy neural networksIEEE Trans Syst Man Cybern Part B20043452144215410.1109/TSMCB.2004.833330 MastorocostasPATheocharisJBA recurrent fuzzy-neural model for dynamic system identificationIEEE Trans Syst Man Cybern200232217619010.1109/3477.990874 ChetverikovDStepanovDKreskPRobust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithmImage Vis Comput20052329930910.1016/j.imavis.2004.05.007 MoriartyDEMiikkulainenREfficient reinforcement learning through symbiotic evolutionMach Learn1996221132 RabbaniTvan den HeuvelFAVosselmannGSegmentation of point clouds using smoothness constraintProc ISPRS200635248253 NarendraKSParthasarathyKIdentification and control of dynamical systems using neural networksIEEE Trans Neural Netw1990142710.1109/72.80202 KozaJKGenetic programming: on the programming of computers by means of natural selection1992Cambridge, MAMIT Press0850.68161 HsuYCLinSFChengYCMulti groups cooperation based symbiotic evolution for TSK-type neuro-fuzzy systems designExp Syst Appl20103775320533010.1016/j.eswa.2010.01.003 TowellGGShavlikJWExtracting refined rules from knowledge-based neural networksMach Learn19931371101 BandyopadhyaySMurthyCAPalSKVGA-classifier: design and applications, IEEE TransSyst Man Cybern Part B200030689089510.1109/3477.891151 CarseBFogartyTCMunroAEvolving fuzzy rule based controllers using genetic algorithmsFuzzy Sets Syst199680327329310.1016/0165-0114(95)00196-4 FogelLJZuradaJMMarksRJIIGoldbergCEvolutionary programming in perspective: the top-down viewComputational intelligence: imitating life1994Piscataway, NJIEEE Press SugenoMTanakaKSuccessive identification of a fuzzy model and its applications to prediction of a complex systemFuzzy Sets Syst199142331533411279780741.9305210.1016/0165-0114(91)90110-C LinCJHsuYCReinforcement hybrid evolutionary learning for recurrent wavelet-based neuro-fuzzy systemsIEEE Trans Fuzzy Syst200715472974510.1109/TFUZZ.2006.889920 LinCTLeeCSGNeural fuzzy systems: a neuro-fuzzy synergism to intelligent system1996Englewood Cliffs, NJPrentice-Hall WangLXMendelJMGenerating fuzzy rules by learning from examplesIEEE Trans Syst Man Cybern199222614141427121246410.1109/21.199466 Riemiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceeding of IEEE international conference on neural networks, pp 586–591 Karr CL (1991) Design of an adaptive fuzzy logic controller using a genetic algorithm. In: Proceeding of the 4th international conference on genetic algorithms, pp 450–457 GoldbergDEGenetic algorithms in search optimization and machine learning1989Reading, MAAddison-Wesley0721.68056 JuangCFLinJYLinCTGenetic reinforcement learning through symbiotic evolution for fuzzy controller designIEEE Trans Syst Man Cybern Part B2000302290302 ChenCHLinCJLinCTUsing an efficient immune symbiotic evolution learning for compensatory neuro-fuzzy controllerIEEE Trans Fuzzy Syst200917366868210.1109/TFUZZ.2008.924186 LeeJTWuHWLeeTYLiuYHChenKTMining closed patterns in multi-sequence time-series databaseData Knowl Eng2009681071109010.1016/j.datak.2009.04.005 GG Towell (943_CR2) 1993; 13 DE Goldberg (943_CR15) 1989 CT Lin (943_CR1) 1996 CJ Lin (943_CR28) 2007; 15 CF Juang (943_CR13) 1999; 10 B Carse (943_CR23) 1996; 80 YC Hsu (943_CR29) 2010; 37 DE Moriarty (943_CR42) 1996; 22 943_CR25 LJ Fogel (943_CR17) 1994 M Li (943_CR18) 2009; 179 943_CR26 943_CR27 SK Tanbeer (943_CR31) 2009; 26 M Sugeno (943_CR36) 1991; 42 CT Lin (943_CR20) 2000; 30 S Bandyopadhyay (943_CR22) 2000; 30 FJ Lin (943_CR8) 2001; 9 C-J Lin (943_CR11) 2004; 34 943_CR40 943_CR7 KS Narendra (943_CR12) 1990; 1 943_CR41 943_CR21 JT Lee (943_CR30) 2009; 68 H Liu (943_CR44) 2006; 70 T Takagi (943_CR5) 1985; 15 SF Lin (943_CR37) 2010; 6 E Cox (943_CR39) 2005 P Besl (943_CR48) 1992; 14 S Shankar (943_CR35) 2009; 4 T Rabbani (943_CR43) 2006; 35 PA Mastorocostas (943_CR14) 2002; 32 LX Wang (943_CR4) 1992; 22 J Zhang (943_CR45) 2008; 26 H Takagi (943_CR9) 1992; 3 CJ Lin (943_CR3) 1997; 5 JK Koza (943_CR16) 1992 D Chetverikov (943_CR46) 2005; 23 O Cordon (943_CR38) 2001 943_CR19 CH Chen (943_CR24) 2009; 17 CF Juang (943_CR6) 1998; 6 YH Liu (943_CR47) 2004; 37 943_CR10 943_CR32 943_CR33 943_CR34 |
| References_xml | – reference: LinCTJouCPGA-based fuzzy reinforcement learning for control of a magnetic bearing systemIEEE Trans Syst Man Cybern Part B2000302276289 – reference: TakagiTSugenoMFuzzy identification of systems and its applications to modeling and controlIEEE Trans Syst Man Cybern1985151161320576.9302110.1109/TSMC.1985.6313399 – reference: Gomez F, Schmidhuber J (2005) Co-evolving recurrent neurons learn deep memory POMDPs. In: Proceeding of conference on genetic and evolutionary computation, pp 491–498 – reference: Agrawal R, Srikant R (1994) Fast algorithm for mining association rules. In: Proceeding of the international conference on VLDB, pp 487–499 – reference: ShankarSPurusothamanTUtility sentient frequent itemset mining and association rule mining: a literature survey and comparative studyInt J Soft Comput Appl200948195 – reference: Riemiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceeding of IEEE international conference on neural networks, pp 586–591 – reference: SugenoMTanakaKSuccessive identification of a fuzzy model and its applications to prediction of a complex systemFuzzy Sets Syst199142331533411279780741.9305210.1016/0165-0114(91)90110-C – reference: HsuYCLinSFChengYCMulti groups cooperation based symbiotic evolution for TSK-type neuro-fuzzy systems designExp Syst Appl20103775320533010.1016/j.eswa.2010.01.003 – reference: CordonOHerreraFHoffmannFMagdalenaLGenetic fuzzy systems evolutionary tuning and learning of fuzzy knowledge bases, advances in fuzzy systems-applications and theory2001NJ, USAWorld Scientific Publishing10.1142/4177 – reference: ChetverikovDStepanovDKreskPRobust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithmImage Vis Comput20052329930910.1016/j.imavis.2004.05.007 – reference: LinCJLinCTAn ART-based fuzzy adaptive learning control networkIEEE Trans Fuzzy Syst19975447749610.1109/91.649900 – reference: Wu YT, An YJ, Geller J, Wu YT (2006) A data mining based genetic algorithm. In: Proceeding of IEEE workshop SEUS-WCCIA, pp 27–28 – reference: Gomez FJ (2003) Robust non-linear control through neuroevolution, Ph. D. Disseration, The University of Texas at Austin – reference: JuangCFLinCTAn on-line self-constructing neural fuzzy inference network and its applicationsIEEE Trans Fuzzy Syst199861123110.1109/91.660805 – reference: TanbeerSKAhmedCFJeongBSParallel and distributed algorithm for frequent pattern mining in large databaseIETE Tech Rev200926556510.4103/0256-4602.48469 – reference: MastorocostasPATheocharisJBA recurrent fuzzy-neural model for dynamic system identificationIEEE Trans Syst Man Cybern200232217619010.1109/3477.990874 – reference: De Jong KA (1975) Analysis of the behavior of a class of genetic adaptive systems, Ph. D. Dissertation, Dep. Computer and Communication Sciences, Univ. Michigan, Ann Arbor, MI – reference: ChenCHLinCJLinCTUsing an efficient immune symbiotic evolution learning for compensatory neuro-fuzzy controllerIEEE Trans Fuzzy Syst200917366868210.1109/TFUZZ.2008.924186 – reference: BeslPMckayNA method for registration of 3-D shapesIEEE Trans Pattern Anal Mach Intell199214223925610.1109/34.121791 – reference: LeeJTWuHWLeeTYLiuYHChenKTMining closed patterns in multi-sequence time-series databaseData Knowl Eng2009681071109010.1016/j.datak.2009.04.005 – reference: LinCJHsuYCReinforcement hybrid evolutionary learning for recurrent wavelet-based neuro-fuzzy systemsIEEE Trans Fuzzy Syst200715472974510.1109/TFUZZ.2006.889920 – reference: MoriartyDEMiikkulainenREfficient reinforcement learning through symbiotic evolutionMach Learn1996221132 – reference: WangLXMendelJMGenerating fuzzy rules by learning from examplesIEEE Trans Syst Man Cybern199222614141427121246410.1109/21.199466 – reference: JuangCFLinCTA recurrent self-organizing neural fuzzy inference networkIEEE Trans Neural Netw199910482884510.1109/72.774232 – reference: CoxEFuzzy modeling and genetic algorithms for data mining and exploration20051San Francisco, USAMorgan Kaufman Publications1113.68072 – reference: Cowder RS (1990) Predicting the mackey-glass time series with cascade-correlation learning. In: Proceedings of the 1990 connectionist models summer school, pp. 117–123 – reference: Mizutani E, Jang J-SR (1995) Coactive neural fuzzy modeling. In: Proceeding of IEEE international conference on neural networks, pp 760–765 – reference: BandyopadhyaySMurthyCAPalSKVGA-classifier: design and applications, IEEE TransSyst Man Cybern Part B200030689089510.1109/3477.891151 – reference: TowellGGShavlikJWExtracting refined rules from knowledge-based neural networksMach Learn19931371101 – reference: JuangCFLinJYLinCTGenetic reinforcement learning through symbiotic evolution for fuzzy controller designIEEE Trans Syst Man Cybern Part B2000302290302 – reference: LinSFChengYCTwo-strategy reinforcement evolutionary algorithm using data-mining based crossover strategy with TSK-type fuzzy controllersInt J Innovat Comput Control20106938633885 – reference: LiMWangZA hybrid coevolutionary algorithm for designing fuzzy classifiersInf Sci2009179121970198310.1016/j.ins.2009.01.045 – reference: ZhangJGeYOngSHChuiCKTeohSHYanCHRapid surface registration of 3D volumes using a neural network approachImage Vis Comput20082620121010.1016/j.imavis.2007.04.003 – reference: KozaJKGenetic programming: on the programming of computers by means of natural selection1992Cambridge, MAMIT Press0850.68161 – reference: LiuYHImproving ICP with easy implementation for free-form surface matchingPattern Recogn2004372112261059.6811110.1016/S0031-3203(03)00239-5 – reference: CarseBFogartyTCMunroAEvolving fuzzy rule based controllers using genetic algorithmsFuzzy Sets Syst199680327329310.1016/0165-0114(95)00196-4 – reference: NarendraKSParthasarathyKIdentification and control of dynamical systems using neural networksIEEE Trans Neural Netw1990142710.1109/72.80202 – reference: LinC-JChinC-CPrediction and identification using wavelet-based recurrent fuzzy neural networksIEEE Trans Syst Man Cybern Part B20043452144215410.1109/TSMCB.2004.833330 – reference: LinCJChenCHLinCTAn efficient evolutionary algorithm for fuzzy inference systemsEvol Syst2011228399 – reference: LiuHYanJZhangDThree-dimensional surface registration: a neural network strategyNeurocomputing20067059760210.1016/j.neucom.2006.04.004 – reference: LinCTLeeCSGNeural fuzzy systems: a neuro-fuzzy synergism to intelligent system1996Englewood Cliffs, NJPrentice-Hall – reference: Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceeding of ACM-SIGMOD, Dallas, Tx, pp 1–12 – reference: FogelLJZuradaJMMarksRJIIGoldbergCEvolutionary programming in perspective: the top-down viewComputational intelligence: imitating life1994Piscataway, NJIEEE Press – reference: Karr CL (1991) Design of an adaptive fuzzy logic controller using a genetic algorithm. In: Proceeding of the 4th international conference on genetic algorithms, pp 450–457 – reference: RabbaniTvan den HeuvelFAVosselmannGSegmentation of point clouds using smoothness constraintProc ISPRS200635248253 – reference: GoldbergDEGenetic algorithms in search optimization and machine learning1989Reading, MAAddison-Wesley0721.68056 – reference: LinFJLinCHShenPHSelf-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor driveIEEE Trans Fuzzy Syst20019575175910.1109/91.963761 – reference: TakagiHSuzukiNKodaTKojimaYNeural networks designed on approximated reasoning architecture and their applicationIEEE Trans Neural Netw19923575275910.1109/72.159063 – volume: 15 start-page: 729 issue: 4 year: 2007 ident: 943_CR28 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2006.889920 – ident: 943_CR10 doi: 10.1109/ICNN.1995.487513 – volume: 6 start-page: 3863 issue: 9 year: 2010 ident: 943_CR37 publication-title: Int J Innovat Comput Control – ident: 943_CR25 doi: 10.1007/s12530-010-9024-8 – volume: 68 start-page: 1071 year: 2009 ident: 943_CR30 publication-title: Data Knowl Eng doi: 10.1016/j.datak.2009.04.005 – volume: 23 start-page: 299 year: 2005 ident: 943_CR46 publication-title: Image Vis Comput doi: 10.1016/j.imavis.2004.05.007 – ident: 943_CR7 doi: 10.1109/ICNN.1993.298623 – volume: 15 start-page: 116 year: 1985 ident: 943_CR5 publication-title: IEEE Trans Syst Man Cybern doi: 10.1109/TSMC.1985.6313399 – ident: 943_CR26 – volume: 37 start-page: 5320 issue: 7 year: 2010 ident: 943_CR29 publication-title: Exp Syst Appl doi: 10.1016/j.eswa.2010.01.003 – volume: 5 start-page: 477 issue: 4 year: 1997 ident: 943_CR3 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/91.649900 – volume: 6 start-page: 12 issue: 1 year: 1998 ident: 943_CR6 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/91.660805 – volume: 10 start-page: 828 issue: 4 year: 1999 ident: 943_CR13 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.774232 – ident: 943_CR41 – volume: 30 start-page: 890 issue: 6 year: 2000 ident: 943_CR22 publication-title: Syst Man Cybern Part B doi: 10.1109/3477.891151 – volume: 4 start-page: 81 year: 2009 ident: 943_CR35 publication-title: Int J Soft Comput Appl – volume: 1 start-page: 4 year: 1990 ident: 943_CR12 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.80202 – volume: 179 start-page: 1970 issue: 12 year: 2009 ident: 943_CR18 publication-title: Inf Sci doi: 10.1016/j.ins.2009.01.045 – volume-title: Genetic algorithms in search optimization and machine learning year: 1989 ident: 943_CR15 – volume: 80 start-page: 273 issue: 3 year: 1996 ident: 943_CR23 publication-title: Fuzzy Sets Syst doi: 10.1016/0165-0114(95)00196-4 – volume: 3 start-page: 752 issue: 5 year: 1992 ident: 943_CR9 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.159063 – volume: 30 start-page: 276 issue: 2 year: 2000 ident: 943_CR20 publication-title: IEEE Trans Syst Man Cybern Part B – volume: 26 start-page: 55 year: 2009 ident: 943_CR31 publication-title: IETE Tech Rev doi: 10.4103/0256-4602.48469 – volume: 17 start-page: 668 issue: 3 year: 2009 ident: 943_CR24 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2008.924186 – ident: 943_CR19 – volume-title: Neural fuzzy systems: a neuro-fuzzy synergism to intelligent system year: 1996 ident: 943_CR1 – volume: 22 start-page: 11 year: 1996 ident: 943_CR42 publication-title: Mach Learn – volume: 14 start-page: 239 issue: 2 year: 1992 ident: 943_CR48 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/34.121791 – volume: 32 start-page: 176 issue: 2 year: 2002 ident: 943_CR14 publication-title: IEEE Trans Syst Man Cybern doi: 10.1109/3477.990874 – ident: 943_CR21 doi: 10.1109/3477.836377 – volume: 22 start-page: 1414 issue: 6 year: 1992 ident: 943_CR4 publication-title: IEEE Trans Syst Man Cybern doi: 10.1109/21.199466 – volume-title: Genetic programming: on the programming of computers by means of natural selection year: 1992 ident: 943_CR16 – volume: 70 start-page: 597 year: 2006 ident: 943_CR44 publication-title: Neurocomputing doi: 10.1016/j.neucom.2006.04.004 – volume-title: Genetic fuzzy systems evolutionary tuning and learning of fuzzy knowledge bases, advances in fuzzy systems-applications and theory year: 2001 ident: 943_CR38 doi: 10.1142/4177 – volume: 9 start-page: 751 issue: 5 year: 2001 ident: 943_CR8 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/91.963761 – volume-title: Computational intelligence: imitating life year: 1994 ident: 943_CR17 – ident: 943_CR40 – volume: 37 start-page: 211 year: 2004 ident: 943_CR47 publication-title: Pattern Recogn doi: 10.1016/S0031-3203(03)00239-5 – ident: 943_CR33 doi: 10.1145/342009.335372 – volume: 26 start-page: 201 year: 2008 ident: 943_CR45 publication-title: Image Vis Comput doi: 10.1016/j.imavis.2007.04.003 – volume-title: Fuzzy modeling and genetic algorithms for data mining and exploration year: 2005 ident: 943_CR39 – volume: 42 start-page: 315 issue: 3 year: 1991 ident: 943_CR36 publication-title: Fuzzy Sets Syst doi: 10.1016/0165-0114(91)90110-C – volume: 34 start-page: 2144 issue: 5 year: 2004 ident: 943_CR11 publication-title: IEEE Trans Syst Man Cybern Part B doi: 10.1109/TSMCB.2004.833330 – ident: 943_CR27 doi: 10.1145/1068009.1068092 – volume: 13 start-page: 71 year: 1993 ident: 943_CR2 publication-title: Mach Learn – ident: 943_CR34 – ident: 943_CR32 – volume: 35 start-page: 248 year: 2006 ident: 943_CR43 publication-title: Proc ISPRS |
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| Snippet | This study proposes a data mining–based hierarchical cooperative coevolutionary algorithm (DMHCCA) for TSK-type neuro-fuzzy networks design. The proposed... |
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| SubjectTerms | Applied sciences Artificial Intelligence Combinatorics Combinatorics. Ordered structures Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer science; control theory; systems Data Mining and Knowledge Discovery Designs and configurations Exact sciences and technology Experimental design Image Processing and Computer Vision Learning and adaptive systems Mathematics Original Article Probability and statistics Probability and Statistics in Computer Science Sciences and techniques of general use Statistics |
| Title | Data mining–based hierarchical cooperative coevolutionary algorithm for TSK-type neuro-fuzzy networks design |
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