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Volumn 21, Issue 6, 2013, Pages 1032-1043

Hierarchical structured sparse representation for t-s fuzzy systems identification

Author keywords

Block structured sparse representation; Fuzzy rules reduction; Fuzzy system identification; Sparse representation

Indexed keywords

BENCHMARK DATASETS; CURSE OF DIMENSIONALITY; FUZZY INFERENCE SYSTEMS; GENERALIZATION PERFORMANCE; SPARSE REGULARIZATIONS; SPARSE REPRESENTATION; STRUCTURED SPARSE REPRESENTATIONS; TAKAGI SUGENO FUZZY SYSTEMS;

EID: 84897744784     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2013.2240690     Document Type: Article
Times cited : (66)

References (60)
  • 2
    • 80053621517 scopus 로고    scopus 로고
    • Dynamic output feedback predictive control for nonlinear systems represented by a takagi-sugeno model
    • Oct
    • B. C. Ding, "Dynamic output feedback predictive control for nonlinear systems represented by a Takagi-Sugeno model," IEEE Trans. Fuzzy Syst., vol. 19, no. 5, pp. 831-843, Oct. 201-1.
    • (2011) IEEE Trans. Fuzzy Syst. , vol.19 , Issue.5 , pp. 831-843
    • Ding, B.C.1
  • 3
    • 80053639817 scopus 로고    scopus 로고
    • Direct model reference takagi-sugeno fuzzy control of siso nonlinear systems
    • Oct
    • M. A. Khanesar, O. Kaynak, andM. Teshnehlab, "Direct model reference Takagi-Sugeno fuzzy control of SISO nonlinear systems," IEEE Trans. Fuzzy Syst., vol. 19, no. 5, pp. 914-924, Oct. 201-1.
    • (2011) IEEE Trans. Fuzzy Syst. , vol.19 , Issue.5 , pp. 914-924
    • Khanesar, M.A.1    Kaynak, O.2    Teshnehlab, M.3
  • 4
    • 0029406441 scopus 로고
    • Building sugeno-type model using fuzzy discretization and orthogonal parameter estimation techniques
    • Nov
    • L. X.Wang and R. Langari, "Building sugeno-type model using fuzzy discretization and orthogonal parameter estimation techniques," IEEE Trans. Fuzzy Syst., vol. 3, no. 4, pp. 454-458, Nov. 199-5.
    • (1995) IEEE Trans. Fuzzy Syst. , vol.3 , Issue.4 , pp. 454-458
    • Wang, L.X.1    Langari, R.2
  • 5
    • 0027601884 scopus 로고
    • Anfis: Adaptive-network-based fuzzy inference systems
    • May
    • J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference systems," IEEE Trans. Syst., Man Cybern., vol. 23, no. 3, pp. 665-685, May 199-3.
    • (1993) IEEE Trans. Syst., Man Cybern. , vol.23 , Issue.3 , pp. 665-685
    • Jang, J.S.R.1
  • 6
    • 84941531642 scopus 로고
    • A new approach to fuzzy-neural system modeling
    • May
    • Y. Lin and G. A. Cunningham, "A new approach to fuzzy-neural system modeling," IEEE Trans. Fuzzy Syst., vol. 3, no. 2, pp. 190-198, May 199-5.
    • (1995) IEEE Trans. Fuzzy Syst. , vol.3 , Issue.2 , pp. 190-198
    • Lin, Y.1    Cunningham, G.A.2
  • 7
    • 33947267506 scopus 로고    scopus 로고
    • Adaptive neuro-fuzzy inference systems based approach to nonlinear noise cancellation for images
    • DOI 10.1016/j.fss.2006.10.028, PII S0165011406004842
    • H. Qin and S. X. Yang, "Adaptive neuro-fuzzy inference systems based approach to nonlinear noise cancelation for images," Fuzzy Sets Syst., vol. 158, pp. 1036-1063, 200-7. (Pubitemid 46428431)
    • (2007) Fuzzy Sets and Systems , vol.158 , Issue.10 , pp. 1036-1063
    • Qin, H.1    Yang, S.X.2
  • 8
    • 0036530967 scopus 로고    scopus 로고
    • Denfis: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction
    • Ar
    • N. Kasabov, "DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction," IEEE Trans. Fuzzy Syst., vol. 10, no. 2, pp. 144-153, Apr. 200-2.
    • (2002) IEEE Trans. Fuzzy Syst. , vol.10 , Issue.2 , pp. 144-153
    • Kasabov, N.1
  • 9
    • 11244351634 scopus 로고    scopus 로고
    • An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network
    • DOI 10.1016/j.fss.2004.03.001, PII S0165011404000843
    • G. Leng, T. M. McGinnity, and G. Prasad, "An approach for on-line extraction of fuzzy rules using a self-organizing fuzzy neural network," Fuzzy Sets Syst., vol. 150, pp. 211-243, 200-5. (Pubitemid 40056019)
    • (2005) Fuzzy Sets and Systems , vol.150 , Issue.2 , pp. 211-243
    • Leng, G.1    McGinnity, T.M.2    Prasad, G.3
  • 10
    • 0346781553 scopus 로고    scopus 로고
    • Ten years of genetic fuzzy systems: Current framework and newtrends
    • 200-4
    • O. Cordon, F. Gomide, F. Herrera, F. Hoffmann, and L. Magdalena, "Ten years of genetic fuzzy systems: Current framework and newtrends," Fuzzy Sets Syst., vol. 141, no. 1, pp. 5-31, 200-4.
    • Fuzzy Sets Syst , vol.141 , Issue.1 , pp. 5-31
    • Cordon, O.1    Gomide, F.2    Herrera, F.3    Hoffmann, F.4    Magdalena, L.5
  • 11
    • 77955504076 scopus 로고    scopus 로고
    • An evolving construction scheme for fuzzy systems
    • Aug
    • D. Wang, X. J. Zeng, and J. A. Keane, "An evolving construction scheme for fuzzy systems," IEEE Trans. Fuzzy Syst., vol. 18, no. 4, pp. 755-770, Aug. 201-0.
    • (2010) IEEE Trans. Fuzzy Syst. , vol.18 , Issue.4 , pp. 755-770
    • Wang, D.1    Zeng, X.J.2    Keane, J.A.3
  • 12
    • 58149502268 scopus 로고    scopus 로고
    • An evolving fuzzy predictor for industrial applications
    • Dec
    • W. Wang and J. Vrbanek, "An evolving fuzzy predictor for industrial applications," IEEE Trans. Fuzzy Syst., vol. 16, no. 6, pp. 1439-1449, Dec. 200-8.
    • (2008) IEEE Trans. Fuzzy Syst. , vol.16 , Issue.6 , pp. 1439-1449
    • Wang, W.1    Vrbanek, J.2
  • 14
    • 34250725862 scopus 로고    scopus 로고
    • Separated antecedent and consequent learning for Takagi-Sugeno fuzzy systems
    • DOI 10.1109/FUZZY.2006.1682014, 1682014, 2006 IEEE International Conference on Fuzzy Systems
    • J. Botzheim, E. Lughofer, E. P. Klement, and L. Ḱoczy, T. D. Gedeon, "Separated antecedent and consequent learning for Takagi-Sugeno fuzzy systems," in Proc. IEEE Int. Conf. Fuzzy Syst., Vancouver, BC, Canada, 2006, pp. 2263-226-9. (Pubitemid 46949057)
    • (2006) IEEE International Conference on Fuzzy Systems , pp. 2263-2269
    • Botzheim, J.1    Lughofer, E.2    Klement, E.P.3    Koczy, L.T.4    Gedeon, T.D.5
  • 16
    • 34548604303 scopus 로고    scopus 로고
    • Comparison of different strategies of utilizing fuzzy clustering in structure identification
    • DOI 10.1016/j.ins.2007.06.030, PII S0020025507003192, Including: Mathematics of Uncertainty
    • K. Kilic, O. Uncu, and I. B. Turksen, "Comparison of different strategies of utilizing fuzzy clustering in structure identification," Inf. Sci., vol. 177, pp. 5153-5162, 200-7. (Pubitemid 47409985)
    • (2007) Information Sciences , vol.177 , Issue.23 , pp. 5153-5162
    • Kilic, K.1    Uncu, O.2    Turksen, I.B.3
  • 17
    • 14644443622 scopus 로고    scopus 로고
    • On the use of the weighted fuzzy c-means in fuzzy modeling
    • DOI 10.1016/j.advengsoft.2004.12.001, PII S0965997804002224
    • G. E. Tsekouras, "On the use of the weighted fuzzy c-means in fuzzy modeling," Adv. Eng. Softw., vol. 36, pp. 287-300, 200-5. (Pubitemid 40308989)
    • (2005) Advances in Engineering Software , vol.36 , Issue.5 , pp. 287-300
    • Tsekouras, G.E.1
  • 18
    • 11244351633 scopus 로고    scopus 로고
    • A hierarchical fuzzy-clustering approach to fuzzy modeling
    • G. E. Tsekouras, H. Sarimveis, E. Kavakli, and G. Bafas, "A hierarchical fuzzy-clustering approach to fuzzy modeling," Fuzzy Sets Syst., vol. 150, pp. 245-267, 200-5.
    • (2005) Fuzzy Sets Syst. , vol.150 , pp. 245-267
    • Tsekouras, G.E.1    Sarimveis, H.2    Kavakli, E.3    Bafas, G.4
  • 19
    • 84974755191 scopus 로고
    • Generation of fuzzy rules by mountain method
    • R. Yager and D. Filev, "Generation of fuzzy rules by mountain method," J. Intell. Fuzzy Syst., vol 2, pp. 209-219, 199-4.
    • (1994) J. Intell. Fuzzy Syst. , vol.2 , pp. 209-219
    • Yager, R.1    Filev, D.2
  • 20
    • 0024701488 scopus 로고
    • Unsupervised optimal fuzzy clustering
    • Jul
    • I. Gath and A. B. Geva, "Unsupervised optimal fuzzy clustering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 773-781, Jul. 198-9.
    • (1989) IEEE Trans. Pattern Anal. Mach. Intell. , vol.11 , Issue.7 , pp. 773-781
    • Gath, I.1    Geva, A.B.2
  • 21
    • 0018057468 scopus 로고
    • Fuzzy clustering with a fuzzy covariance matrix
    • Adaptive Processes, San Diego, CA, USA, Jan
    • E. E. Gustafson and W. C. Kessel, "Fuzzy clustering with a fuzzy covariance matrix," in Proc. IEEE Conf. Decision Control Including 17th Symp. Adaptive Processes, San Diego, CA, USA, Jan. 1979, pp. 761-76-6.
    • (1979) Proc. IEEE Conf. Decision Control Including 17th Symp. , pp. 761-766
    • Gustafson, E.E.1    Kessel, W.C.2
  • 22
    • 35448950018 scopus 로고    scopus 로고
    • Extensions of vector quantization for incremental clustering
    • DOI 10.1016/j.patcog.2007.07.019, PII S0031320307003354, Feature Generation and Machine Learning for Robust Multimodal Biometrics
    • E. Lughofer, "Extensions of vector quantization for incremental clustering," Pattern Recognit., vol. 41, no. 3, pp. 995-1011, 200-8. (Pubitemid 47632671)
    • (2008) Pattern Recognition , vol.41 , Issue.3 , pp. 995-1011
    • Lughofer, E.1
  • 23
    • 84974743850 scopus 로고
    • Fuzzy model identification based on cluster estimation
    • S. Chiu, "Fuzzy model identification based on cluster estimation," J. Intell. Fuzzy Syst., vol. 2, no. 3, pp. 267-278, 199-4.
    • (1994) J. Intell. Fuzzy Syst. , vol.2 , Issue.3 , pp. 267-278
    • Chiu, S.1
  • 24
    • 0033078615 scopus 로고    scopus 로고
    • Simplifying fuzzy rule-based models using orthogonal transformation methods
    • Feb
    • J. Yen and L. Wang, "Simplifying fuzzy rule-based models using orthogonal transformation methods," IEEE Trans. Syst. Man Cybern., vol. 29, no. 1, pp. 13-24, Feb. 199-9.
    • (1999) IEEE Trans. Syst. Man Cybern. , vol.29 , Issue.1 , pp. 13-24
    • Yen, J.1    Wang, L.2
  • 25
    • 54049155041 scopus 로고    scopus 로고
    • Total least squares in fuzzy system identification: An application to an industrial engine
    • S. Jakubek, C. Hametner, and N. Keuth, "Total least squares in fuzzy system identification: An application to an industrial engine," Eng. Appl. Artif. Intell., vol. 21, no. 8, pp. 1277-1288, 200-8.
    • (2008) Eng. Appl. Artif. Intell. , vol.21 , Issue.8 , pp. 1277-1288
    • Jakubek, S.1    Hametner, C.2    Keuth, N.3
  • 26
    • 0026928374 scopus 로고
    • Fuzzy basis functions, universal approximation and orthogonal least-squares learning
    • Se
    • L. X. Wang and J. M. Mendel, "Fuzzy basis functions, universal approximation and orthogonal least-squares learning," IEEE Trans. Neural Netw., vol. 3, no. 5, pp. 807-814, Sep. 199-2.
    • (1992) IEEE Trans. Neural Netw. , vol.3 , Issue.5 , pp. 807-814
    • Wang, L.X.1    Mendel, J.M.2
  • 27
    • 34447298360 scopus 로고    scopus 로고
    • Building an interpretable fuzzy rule base from data using orthogonal least squares- application to a depollution problem
    • 200-7
    • S. Destercke, S. Guillaume, and B. Charnomordic, "Building an interpretable fuzzy rule base from data using orthogonal least squares- application to a depollution problem," Fuzzy Sets Syst., vol. 158, no. 18, pp. 2078-2094, 200-7.
    • Fuzzy Sets Syst , vol.158 , Issue.18 , pp. 2078-2094
    • Destercke, S.1    Guillaume, S.2    Charnomordic, B.3
  • 28
    • 0033115927 scopus 로고    scopus 로고
    • Reduction of fuzzy rule base via singular value decomposition
    • Ar
    • Y. Yam, P. Baranyi, and C. T. Yang, "Reduction of fuzzy rule base via singular value decomposition," IEEE Trans. Fuzzy Syst., vol. 7, no. 2, pp. 120-131, Apr. 199-9.
    • (1999) IEEE Trans. Fuzzy Syst. , vol.7 , Issue.2 , pp. 120-131
    • Yam, Y.1    Baranyi, P.2    Yang, C.T.3
  • 30
    • 1942423639 scopus 로고    scopus 로고
    • Tp model transformation as a way to lmi based controller design
    • Ar
    • P. Baranyi, "TP model transformation as a way to LMI based controller design," IEEE Trans. Ind. Electron., vol. 51, no. 2, pp. 387-400, Apr. 200-4.
    • (2004) IEEE Trans. Ind. Electron. , vol.51 , Issue.2 , pp. 387-400
    • Baranyi, P.1
  • 31
    • 33744797404 scopus 로고    scopus 로고
    • Case study of the TP-model transformation in the control of a complex dynamic model with structural nonlinearity
    • DOI 10.1109/TIE.2006.874265
    • P. Baranyi and Y. Yam, "Case study of the TP model transformation in the control design of a complex dynamic model with structural non-linearity," IEEE Trans. Ind. Electron., vol. 53. no. 3, pp. 895-904, Jun. 200-6. (Pubitemid 43834609)
    • (2006) IEEE Transactions on Industrial Electronics , vol.53 , Issue.3 , pp. 895-904
    • Baranyi, P.1    Yam, Y.2
  • 32
    • 0042700003 scopus 로고    scopus 로고
    • From differential equations to pdc controller design via numerical transformation
    • P. Baranyi, D. Tikk, Y. Yam, and R. J. Patton, "From differential equations to PDC controller design via numerical transformation," Comput. Ind., vol. 51, pp. 281-297, 200-3.
    • (2003) Comput. Ind. , vol.51 , pp. 281-297
    • Baranyi, P.1    Tikk, D.2    Yam, Y.3    Patton, R.J.4
  • 33
    • 35549007294 scopus 로고    scopus 로고
    • Approximation properties of TP model forms and its consequences to TPDC design framework
    • D. Tikk, P. Baranyi, and R. J. Patton, "Approximation properties of TP model forms and its consequents to TPDC design framework," Asian J. Contr., vol. 9, no. 3, pp. 221-231, 200-7. (Pubitemid 350010477)
    • (2007) Asian Journal of Control , vol.9 , Issue.3 , pp. 221-231
    • Tikk, D.1    Baranyi, P.2    Patton, R.J.3
  • 34
    • 0034476279 scopus 로고    scopus 로고
    • Representing membership functions as points in high-dimensional spaces for fuzzy interpolation and extrapolation
    • DOI 10.1109/91.890335
    • Y. Yamand L. T. Ḱoczy, "Representingmembership functions as points in high-dimensional spaces for fuzzy interpolation and extrapolation," IEEE Trans. Fuzzy Syst., vol. 8, no. 6, pp. 761-772, Dec. 200-0. (Pubitemid 32130577)
    • (2000) IEEE Transactions on Fuzzy Systems , vol.8 , Issue.6 , pp. 761-772
    • Yam, Y.1    Koczy, L.T.2
  • 35
    • 30344465742 scopus 로고    scopus 로고
    • Fuzzy rule interpolation for multidimensional input spaces with applications: A case study
    • DOI 10.1109/TFUZZ.2005.859316
    • K. W.Wong, D. Tikk, T. D. Gedeon, and L. T. Ḱoczy, "Rule interpolation for multidimensional input spaces with applications: A case study," IEEE Trans. Fuzzy Syst., vol. 13, no. 6, pp. 809-819, Dec. 200-5. (Pubitemid 43065964)
    • (2005) IEEE Transactions on Fuzzy Systems , vol.13 , Issue.6 , pp. 809-819
    • Wong, K.W.1    Tikk, D.2    Gedeon, T.D.3    Koczy, L.T.4
  • 36
    • 10944265590 scopus 로고
    • Rule interpolation by α-level sets in fuzzy approximate reasoning
    • L. T. Ḱoczy, K. Hirota, "Rule interpolation by α-level sets in fuzzy approximate reasoning," Bulletin Stud. Exchanges Fuzz. Appl., vol. 46, pp. 115-123, 199-1.
    • (1991) Bulletin Stud. Exchanges Fuzz. Appl. , vol.46 , pp. 115-123
    • Ḱoczy, L.T.1    Hirota, K.2
  • 37
    • 10944267254 scopus 로고    scopus 로고
    • A generalized concept for fuzzy rule interpolation
    • Dec
    • P. Baranyi, L. T. Ḱoczy, and T. D. Gedeon, "A generalized concept for fuzzy rule interpolation," IEEE Trans. Fuzzy Syst., vol. 12, no. 6, pp. 820- 832, Dec. 200-4.
    • (2004) IEEE Trans. Fuzzy Syst. , vol.12 , Issue.6 , pp. 820-832
    • Baranyi, P.1    Ḱoczy, L.T.2    Gedeon, T.D.3
  • 38
    • 0034204783 scopus 로고    scopus 로고
    • Comprehensive analysis of a new fuzzy rule interpolation method
    • Jun
    • D. Tikk and P. Baranyi, "Comprehensive analysis of a new fuzzy rule interpolation method," IEEE Trans. Fuzzy Syst., vol. 8, no. 3, pp. 281- 296, Jun. 200-0.
    • (2000) IEEE Trans. Fuzzy Syst. , vol.8 , Issue.3 , pp. 281-296
    • Tikk, D.1    Baranyi, P.2
  • 39
    • 77649256868 scopus 로고    scopus 로고
    • Hosvd based canonical form for polytopic models of dynamic systems
    • L. Szeidl and P. V́arlaki, "HOSVD based canonical form for polytopic models of dynamic systems," J. Adv. Comput. Intell. Inf., vol. 13, no. 1, pp. 52-60, 200-9.
    • (2009) J. Adv. Comput. Intell. Inf. , vol.13 , Issue.1 , pp. 52-60
    • Szeidl, L.1    V́arlaki, P.2
  • 40
    • 77950638805 scopus 로고    scopus 로고
    • Sparsefis: Data-driven learning of fuzzy systems with sparsity constraints
    • Ar
    • E. Lughofer and S. Kindermann, "SparseFIS: Data-driven learning of fuzzy systems with sparsity constraints," IEEE Trans. Fuzzy Syst., vol. 18, no. 2, pp. 396-411, Apr. 201-0.
    • (2010) IEEE Trans. Fuzzy Syst. , vol.18 , Issue.2 , pp. 396-411
    • Lughofer, E.1    Kindermann, S.2
  • 42
    • 80051715867 scopus 로고    scopus 로고
    • Grouped orthogonal matching pursuit for variable selection and prediction
    • A. C. Lozano and G. Swirszcz, N. Abe, "Grouped orthogonal matching pursuit for variable selection and prediction," in Proc. Neural Inform. Process. Syst., 200-9.
    • (2009) Proc. Neural Inform. Process. Syst.
    • Lozano, A.C.1    Swirszcz, G.2
  • 43
    • 77958006903 scopus 로고    scopus 로고
    • Fast group sparse classification
    • M. Angshul and K. Rabab, "Fast group sparse classification," Can. J. Electr. Comput. Eng., vol. 34, no. 4, pp. 136-144, 200-9.
    • (2009) Can. J. Electr. Comput. Eng. , vol.34 , Issue.4 , pp. 136-144
    • Angshul, M.1    Rabab, K.2
  • 44
    • 84871694297 scopus 로고    scopus 로고
    • Structured sparse representation appearance model for robust visual tracking
    • May
    • T. X. Bai, Y. F. Li, and Y. Z Tang, "Structured sparse representation appearance model for robust visual tracking," in Proc. Int. Conf. Robot. Autom., May 2011, pp. 4399-440-4.
    • (2011) Proc. Int. Conf. Robot. Autom. , pp. 4399-4404
    • Bai, T.X.1    Li, Y.F.2    Tang, Y.Z.3
  • 46
    • 64649083745 scopus 로고    scopus 로고
    • Signal recovery from random measurements via orthogonal matching pursuit
    • Dec
    • J. A. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Inf. Theor., vol. 53, no. 12, pp. 4655-4666, Dec. 200-7.
    • (2007) IEEE Trans. Inf. Theor. , vol.53 , Issue.12 , pp. 4655-4666
    • Tropp, J.A.1    Gilbert, A.C.2
  • 47
    • 64149088421 scopus 로고    scopus 로고
    • On the consistency of feature selection using greedy least squares regression
    • T. Zhang, "On the consistency of feature selection using greedy least squares regression," J. Mach. Learn. Res., vol. 10, pp. 555-568, 200-9.
    • (2009) J. Mach. Learn. Res. , vol.10 , pp. 555-568
    • Zhang, T.1
  • 48
    • 0032205732 scopus 로고    scopus 로고
    • Improving the interpretability of TSK fuzzy models by combining global learning and local learning
    • PII S1063670698082629
    • J. Yen, L. Wang, and C. W. Gillespie, "Improving the interpretability of TSK fuzzy models by combining global learning and local learning," IEEE Trans. Fuzzy Syst., vol. 6, no. 4, pp. 530-537, Nov. 199-8. (Pubitemid 128750083)
    • (1998) IEEE Transactions on Fuzzy Systems , vol.6 , Issue.4 , pp. 530-537
    • Yen, J.1    Wang, L.2    Gillespie, C.W.3
  • 50
    • 84890520049 scopus 로고    scopus 로고
    • Use of the zero normwith linear models and kernel methods
    • J.Weston, A. Elisseeff, andB. Scholkopf, "Use of the zero normwith linear models and kernel methods," J. Mach. Learn. Res., vol. 3, pp. 1439-1461, 200-3.
    • (2003) J. Mach. Learn. Res. , vol.3 , pp. 1439-1461
    • Weston, J.1    Elisseeff, A.2    Scholkopf, B.3
  • 51
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • R. Tibshirani, "Regression shrinkage and selection via the lasso," J. Roy. Statist. Soc. B, vol. 58, pp. 267-288, 199-6.
    • (1996) J. Roy. Statist. Soc. B , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 52
    • 84950445313 scopus 로고
    • Cross-validation of regression models
    • R. Picard and D. Cook, "Cross-validation of regression models," J. Amer. Statist. Assoc., vol. 79, no. 387, pp. 575-583, 198-4.
    • (1984) J. Amer. Statist. Assoc. , vol.79 , Issue.387 , pp. 575-583
    • Picard, R.1    Cook, D.2
  • 53
    • 85164392958 scopus 로고
    • A study of cross-validation and bootstrap for accuracy estimation and model selection
    • R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Proc. 14th Int. Joint Conf. Artif. Intell., 1995, vol. 2, no. 12, pp. 1137-114-5.
    • (1995) Proc. 14th Int. Joint Conf. Artif. Intell. , vol.2 , Issue.12 , pp. 1137-1145
    • Kohavi, R.1
  • 54
    • 0032164985 scopus 로고    scopus 로고
    • A simply identified Sugeno-type fuzzy model via double clustering
    • PII S0020025597100834
    • E. Kim, H. Lee, and M. Park, "A simply identified Sugeno-type fuzzy model via double clustering," Inf. Sci. vol. 110, no. 1/2, pp. 25-39, 199-8. (Pubitemid 128369951)
    • (1998) Information Sciences , vol.110 , Issue.1-2 , pp. 25-39
    • Kim, E.1    Lee, H.2    Park, M.3    Park, M.4
  • 55
    • 0034300570 scopus 로고    scopus 로고
    • Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems
    • S. K. Oh and W. Pedrycz, "Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems," Fuzzy Sets Syst., vol. 115, no. 2, pp. 205-230, 200-0.
    • (2000) Fuzzy Sets Syst. , vol.115 , Issue.2 , pp. 205-230
    • Oh, S.K.1    Pedrycz, W.2
  • 56
    • 84863251575 scopus 로고    scopus 로고
    • Identification of fuzzy inference systems using a multi-objective space search algorithm and information granulation
    • W. Huang, S. K. Oh, L. Ding, H. K. Kim, and S. C. Joo, "Identification of fuzzy inference systems using a multi-objective space search algorithm and information granulation," J. Electr. Eng., vol. 6, no. 6, pp. 853-866, 201-1.
    • (2011) J. Electr. Eng. , vol.6 , Issue.6 , pp. 853-866
    • Huang, W.1    Oh, S.K.2    Ding, L.3    Kim, H.K.4    Joo, S.C.5
  • 57
    • 50149097761 scopus 로고    scopus 로고
    • Identification of fuzzy models using a successive tuning method with a variant identification ratio
    • J. Choi, S. K. Oh, and W. Pedrycz, "Identification of fuzzy models using a successive tuning method with a variant identification ratio," Fuzzy Sets Syst., vol. 159, no. 21, pp. 2873-2889, 200-8.
    • (2008) Fuzzy Sets Syst. , vol.159 , Issue.21 , pp. 2873-2889
    • Choi, J.1    Oh, S.K.2    Pedrycz, W.3
  • 58
    • 70350605478 scopus 로고    scopus 로고
    • Data-driven fuzzy modeling for takagi-sugeno-kang fuzzy system
    • B. Rezaee and M. H. Fazel Zarandi, "Data-driven fuzzy modeling for takagi-sugeno-kang fuzzy system," Inf. Sci. vol. 180, pp. 241-255, 201-0.
    • (2010) Inf. Sci. , vol.180 , pp. 241-255
    • Rezaee, B.1    Fazel Zarandi, M.H.2
  • 59
    • 84859722569 scopus 로고    scopus 로고
    • T-s fuzzy model identification with gravitational search based hyper-plane clustering algorithm
    • Ar
    • C. S. Li, J. Z. Zhou, B. Fu, P. Kou, and J. Xiao, "T-S fuzzy model identification with gravitational search based hyper-plane clustering algorithm," IEEE Trans. Fuzzy Syst., vol. 20, no. 2, pp. 305-317, Apr. 201-2.
    • (2012) IEEE Trans. Fuzzy Syst. , vol.20 , Issue.2 , pp. 305-317
    • Li, C.S.1    Zhou, J.Z.2    Fu, B.3    Kou, P.4    Xiao, J.5
  • 60
    • 0036791593 scopus 로고    scopus 로고
    • Modified gath-geva fuzzy clustering for identification of takagi-sugeno fuzzy models
    • Oct
    • J. Abonyi and R. Babusska, F. Szeifert, "Modified gath-geva fuzzy clustering for identification of takagi-sugeno fuzzy models," IEEE Trans. Syst. Man Cybern., vol. 32, no. 5, pp. 612-621, Oct. 200-2.
    • (2002) IEEE Trans. Syst. Man Cybern. , vol.32 , Issue.5 , pp. 612-621
    • Abonyi, J.1    Babusska, R.2    Szeifert, F.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.