메뉴 건너뛰기




Volumn 1, Issue 6, 2011, Pages 461-476

Evolving fuzzy systems for data streams: A survey

Author keywords

[No Author keywords available]

Indexed keywords

DATA STREAMS; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; INTELLIGENT SYSTEMS; ONLINE SYSTEMS;

EID: 84867604848     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.42     Document Type: Article
Times cited : (22)

References (63)
  • 1
    • 34250724315 scopus 로고    scopus 로고
    • Available at, (Accessed March 5, 2010)
    • Angelov P. Evolving fuzzy systems. Available at: http://www.scholarpedia.org/article/Evolvingfuzzysystems. (Accessed March 5, 2010).
    • Evolving fuzzy systems
    • Angelov, P.1
  • 3
    • 0141629793 scopus 로고    scopus 로고
    • Evolving Connectionist Sytems: Methods and Applictions in Bioinformatics
    • London: Springer-Verlag
    • Kasabov N. Evolving Connectionist Sytems: Methods and Applictions in Bioinformatics, Brian Study and Intelligent Machines. London: Springer-Verlag; 2003.
    • (2003) Brian Study and Intelligent Machines
    • Kasabov, N.1
  • 4
    • 0042166469 scopus 로고    scopus 로고
    • Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems
    • Angelov P. Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems. Heidelberg: Physica-Verlag; 2002.
    • (2002) Heidelberg: Physica-Verlag
    • Angelov, P.1
  • 7
    • 85169706321 scopus 로고    scopus 로고
    • The ECOS framework and the ECO learning method for evolving connectionist systems
    • Kasabov N. The ECOS framework and the ECO learning method for evolving connectionist systems. J Adv Comput Intell 1998, 2:195-202.
    • (1998) J Adv Comput Intell , vol.2 , pp. 195-202
    • Kasabov, N.1
  • 8
    • 0017703889 scopus 로고    scopus 로고
    • Application of fuzzy logic to approximate reasoning using linguistic systems
    • Mamdani EH. Application of fuzzy logic to approximate reasoning using linguistic systems. Fuzzy Sets Syst 1997, 26:1182-1191.
    • (1997) Fuzzy Sets Syst , vol.26 , pp. 1182-1191
    • Mamdani, E.H.1
  • 9
    • 0021892282 scopus 로고
    • Fuzzy identification of systems and its application to modelling and control
    • Takagi T, Sugeno M. Fuzzy identification of systems and its application to modelling and control. IEEE Trans Syst Man Cybern 1985, 15:116-132.
    • (1985) IEEE Trans Syst Man Cybern , vol.15 , pp. 116-132
    • Takagi, T.1    Sugeno, M.2
  • 10
    • 0026943536 scopus 로고
    • Generating fuzzy rules by learning from examples
    • Wang LX, Mendel JM. Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 1992, 22:1414-1427.
    • (1992) IEEE Trans Syst Man Cybern , vol.22 , pp. 1414-1427
    • Wang, L.X.1    Mendel, J.M.2
  • 11
    • 0027601884 scopus 로고
    • ANFIS: adaptive-network-based fuzzy inference system
    • Jang JSR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 1993, 23:665-685.
    • (1993) IEEE Trans Syst Man Cybern , vol.23 , pp. 665-685
    • Jang, J.S.R.1
  • 12
    • 0027266182 scopus 로고
    • Functional equivalence between radial bsis function networks and fuzzy inference systems
    • Jang J-SR, Sun C-T. Functional equivalence between radial bsis function networks and fuzzy inference systems. IEEE Trans Neural Netw 1993, 4:156-159.
    • (1993) IEEE Trans Neural Netw , vol.4 , pp. 156-159
    • Jang, J.-S.R.1    Sun, C.-T.2
  • 13
    • 0003653353 scopus 로고
    • Knowledge Aquisition: Principles and Guidelines
    • NJ: Prentice Hall
    • McGraw KL, Harbinson-Briggs KH. Knowledge Aquisition: Principles and Guidelines. Englewood Cliffs, NJ: Prentice Hall; 1989.
    • (1989) Englewood Cliffs
    • McGraw, K.L.1    Harbinson-Briggs, K.H.2
  • 14
    • 77949774043 scopus 로고    scopus 로고
    • Adaptive inferential sensors based on evolving fuzzy models: an industrial case study
    • Angelov P, Kordon A. Adaptive inferential sensors based on evolving fuzzy models: an industrial case study. IEEE Trans Syst Man Cybern-B 2010, 40:529-539.
    • (2010) IEEE Trans Syst Man Cybern-B , vol.40 , pp. 529-539
    • Angelov, P.1    Kordon, A.2
  • 15
    • 58149524918 scopus 로고    scopus 로고
    • Evolving fuzzy-rule-based classifiers from data streams
    • Angelov P, Zhou X. Evolving fuzzy-rule-based classifiers from data streams. IEEE Trans Fuzzy Syst 2008, 16:1462-1475.
    • (2008) IEEE Trans Fuzzy Syst , vol.16 , pp. 1462-1475
    • Angelov, P.1    Zhou, X.2
  • 17
    • 0030126609 scopus 로고    scopus 로고
    • Learning in the presence of concept drift and hidden contexts
    • Widmer G, Kubat M. Learning in the presence of concept drift and hidden contexts. Mach Learn 1996, 23:69-101.
    • (1996) Mach Learn , vol.23 , pp. 69-101
    • Widmer, G.1    Kubat, M.2
  • 18
    • 1242310003 scopus 로고    scopus 로고
    • Incremental learning with partial instance memory
    • Maloof M, Michalski R. Incremental learning with partial instance memory. Artif Intell 2004, 154:95-126.
    • (2004) Artif Intell , vol.154 , pp. 95-126
    • Maloof, M.1    Michalski, R.2
  • 20
    • 11144352228 scopus 로고    scopus 로고
    • On-line identification of MIMO evolving Takagi-Sugeno fuzzy models
    • Budapest: IEEE
    • Angelov P, Xydeas C, Filev D. On-line identification of MIMO evolving Takagi-Sugeno fuzzy models. In: IJCNN-FUZZ-IEEE. Vol. 1. Budapest: IEEE; 2004, 55-60.
    • (2004) IJCNN-FUZZ-IEEE , vol.1 , pp. 55-60
    • Angelov, P.1    Xydeas, C.2    Filev, D.3
  • 21
  • 23
    • 0033130756 scopus 로고    scopus 로고
    • Finding relevant attributes and membership functions
    • Hong T-P, Chen J-B. Finding relevant attributes and membership functions. Fuzzy Sets Syst 1999, 103:389-404.
    • (1999) Fuzzy Sets Syst , vol.103 , pp. 389-404
    • Hong, T.-P.1    Chen, J.-B.2
  • 24
    • 0028735390 scopus 로고
    • A fuzzy approach to input variable identification
    • Orlando, Fl: IEEE
    • Lin Y, Cunningham GA. A fuzzy approach to input variable identification. In: IEEE Conference on Fuzzy Systems. Vol. 3. Orlando, Fl: IEEE; 1994, 2031-2036.
    • (1994) IEEE Conference on Fuzzy Systems , vol.3 , pp. 2031-2036
    • Lin, Y.1    Cunningham, G.A.2
  • 25
    • 0000903874 scopus 로고    scopus 로고
    • Soft computing for feature analysis
    • Pal NR. Soft computing for feature analysis. Fuzzy Sets Syst 1999, 103:201-221.
    • (1999) Fuzzy Sets Syst , vol.103 , pp. 201-221
    • Pal, N.R.1
  • 26
    • 0030291740 scopus 로고    scopus 로고
    • An overview of fuzzy modeling for control
    • Babuška R, Verbruggen HB. An overview of fuzzy modeling for control. Control Eng Pract 1996, 4:1593-1606.
    • (1996) Control Eng Pract , vol.4 , pp. 1593-1606
    • Babuška, R.1    Verbruggen, H.B.2
  • 27
    • 84974743850 scopus 로고
    • Fuzzy model identification based on cluster estimation
    • Chiu SL. Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 1994, 2:267-278.
    • (1994) J Intell Fuzzy Syst , vol.2 , pp. 267-278
    • Chiu, S.L.1
  • 29
    • 77957852211 scopus 로고    scopus 로고
    • A simple rule-based system through vector membership and kernel-based granulation
    • London: IEEE Press
    • Angelov P, Yager R. A simple rule-based system through vector membership and kernel-based granulation. In: Fifth International Conference on Intelligent Systems, IS-2010. London: IEEE Press; 2010, 349-354.
    • (2010) Fifth International Conference on Intelligent Systems, IS-2010 , pp. 349-354
    • Angelov, P.1    Yager, R.2
  • 30
    • 0036802284 scopus 로고    scopus 로고
    • Identification of evolving fuzzy rule-based models
    • Angelov P, Buswell R. Identification of evolving fuzzy rule-based models. IEEE Trans Fuzzy Syst 2002, 10:667-677.
    • (2002) IEEE Trans Fuzzy Syst , vol.10 , pp. 667-677
    • Angelov, P.1    Buswell, R.2
  • 33
    • 84886838236 scopus 로고    scopus 로고
    • Evolving Takagi-Sugeno fuzzy systems from streaming data (eTS+)
    • Hoboken, NJ: John Wiley & Sons
    • Angelov P. Evolving Takagi-Sugeno fuzzy systems from streaming data (eTS+). In: Angelov P, Filev D, Kasabov N, eds. Evolving Intelligent Systems. Hoboken, NJ: John Wiley & Sons; 2010, 21-50.
    • (2010) Angelov P, Filev D, Kasabov N, eds. Evolving Intelligent Systems , pp. 21-50
    • Angelov, P.1
  • 34
    • 55249122198 scopus 로고    scopus 로고
    • FLEXFIS: a robust incremental learning approach for evolving Takagi-Sugeno fuzzy models
    • Lughofer ED. FLEXFIS: a robust incremental learning approach for evolving Takagi-Sugeno fuzzy models. IEEE Trans Fuzzy Syst 2008, 16:1393-1410.
    • (2008) IEEE Trans Fuzzy Syst , vol.16 , pp. 1393-1410
    • Lughofer, E.D.1
  • 37
    • 0028482884 scopus 로고
    • Approximate clustering via mountain method
    • Yager R, Filev D. Approximate clustering via mountain method. IEEE Trans Syst Man and Cybern 1994, 24:1279-1284.
    • (1994) IEEE Trans Syst Man and Cybern , vol.24 , pp. 1279-1284
    • Yager, R.1    Filev, D.2
  • 39
    • 0032205732 scopus 로고    scopus 로고
    • Improving the interpretability of TSK fuzzy models by combining global learning and local learning
    • Yen J, Wang L, Gillespie CW. Improving the interpretability of TSK fuzzy models by combining global learning and local learning. IEEE Trans Fuzzy Syst 1998, 6:530-537.
    • (1998) IEEE Trans Fuzzy Syst , vol.6 , pp. 530-537
    • Yen, J.1    Wang, L.2    Gillespie, C.W.3
  • 40
    • 0025491490 scopus 로고
    • A model of participatory learning
    • Yager R. A model of participatory learning. IEEE Trans Syst Man Cybern 1990, 20:1229-1234.
    • (1990) IEEE Trans Syst Man Cybern , vol.20 , pp. 1229-1234
    • Yager, R.1
  • 42
    • 35448950018 scopus 로고    scopus 로고
    • Extensions of vector quantization for incremental clustering
    • Lughofer ED. Extensions of vector quantization for incremental clustering. Pattern Recognit 2008, 41:995-1011.
    • (2008) Pattern Recognit , vol.41 , pp. 995-1011
    • Lughofer, E.D.1
  • 44
    • 0028748949 scopus 로고
    • Growing cell sructures-A self-organizing network for unsupervised and supervised learning
    • Fritzke B. Growing cell sructures-A self-organizing network for unsupervised and supervised learning. Neural Netw 1994, 7:1441-1460.
    • (1994) Neural Netw , vol.7 , pp. 1441-1460
    • Fritzke, B.1
  • 46
    • 0036789790 scopus 로고    scopus 로고
    • A self-organising network that grows when required
    • Marsland S, Shapiro J, Nehmzow U. A self-organising network that grows when required. Neural Netw 2002, 15:1041-1058.
    • (2002) Neural Netw , vol.15 , pp. 1041-1058
    • Marsland, S.1    Shapiro, J.2    Nehmzow, U.3
  • 47
    • 0035670764 scopus 로고    scopus 로고
    • Evolving fuzzy neural networks for supervised/ unsupervised online knowledge-based learning
    • Kasabov N. Evolving fuzzy neural networks for supervised/ unsupervised online knowledge-based learning. IEEE Trans Syst Man Cybern-Part B 2001, 31:902-918.
    • (2001) IEEE Trans Syst Man Cybern-Part B , vol.31 , pp. 902-918
    • Kasabov, N.1
  • 48
    • 0036530967 scopus 로고    scopus 로고
    • DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction
    • Kasabov N, Song Q. DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst 2002, 10:144-154.
    • (2002) IEEE Trans Fuzzy Syst , vol.10 , pp. 144-154
    • Kasabov, N.1    Song, Q.2
  • 49
    • 11244351634 scopus 로고    scopus 로고
    • An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network
    • Leng G, McGinnity TM, Prasad G. An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. Fuzzy Sets Syst 2005, 150:211-243.
    • (2005) Fuzzy Sets Syst , vol.150 , pp. 211-243
    • Leng, G.1    McGinnity, T.M.2    Prasad, G.3
  • 50
    • 33645070541 scopus 로고    scopus 로고
    • Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction
    • Rong H-J, Sundararajan N, Huang G-B, Saratchandran P. Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets Syst 2006, 157:1260-1275.
    • (2006) Fuzzy Sets Syst , vol.157 , pp. 1260-1275
    • Rong, H.-J.1    Sundararajan, N.2    Huang, G.-B.3    Saratchandran, P.4
  • 52
    • 0025404409 scopus 로고
    • Fuzzy logic in control systems: fuzzy logic controller-part I and II
    • Lee CC. Fuzzy logic in control systems: fuzzy logic controller-part I and II. IEEE Trans Syst Man Cybern 1990, 20:404-435.
    • (1990) IEEE Trans Syst Man Cybern , vol.20 , pp. 404-435
    • Lee, C.C.1
  • 54
    • 0035131889 scopus 로고    scopus 로고
    • A pruning method for the recursive least squared algorithm
    • Leung CS, Wong KW, Sum PF, Chan LW. A pruning method for the recursive least squared algorithm. Neural Netw 2001, 14:147-174.
    • (2001) Neural Netw , vol.14 , pp. 147-174
    • Leung, C.S.1    Wong, K.W.2    Sum, P.F.3    Chan, L.W.4
  • 55
    • 34548793665 scopus 로고    scopus 로고
    • Autonomous visual selflocalization in completely unknown environment using evolving fuzzy rule-based calssifier
    • Hawaii, USA: IEEE
    • Zhou X, Angelov P. Autonomous visual selflocalization in completely unknown environment using evolving fuzzy rule-based calssifier. In: IEEE Symposium on Computational Intelligence in Security and Defense Applications. Hawaii, USA: IEEE; 2007, 131-138.
    • (2007) IEEE Symposium on Computational Intelligence in Security and Defense Applications , pp. 131-138
    • Zhou, X.1    Angelov, P.2
  • 57
    • 79952352233 scopus 로고    scopus 로고
    • Evolving classification of agents' behaviors: a general approach
    • Iglesias JA, Angelov P, Ledezma A, Sanchis A. Evolving classification of agents' behaviors: a general approach. Evolving Syst 2010, 1:161-171.
    • (2010) Evolving Syst , vol.1 , pp. 161-171
    • Iglesias, J.A.1    Angelov, P.2    Ledezma, A.3    Sanchis, A.4
  • 59
    • 77950917666 scopus 로고    scopus 로고
    • On-line evolving image classifiers and their application to surface inspection
    • Lughofer E. On-line evolving image classifiers and their application to surface inspection. Image Vision Comput 2010, 28:1065-1079.
    • (2010) Image Vision Comput , vol.28 , pp. 1065-1079
    • Lughofer, E.1
  • 60
    • 77953290010 scopus 로고    scopus 로고
    • A self-learning fuzzy classifier with feature selection for intelligent interrogation of mid-IR spectroscopy data derived from different categories of exfoliative cervical cytology
    • Special Issue on the Future of Fuzzy Systems Research
    • Kelly J, Angelov P, Walsh MJ, Pollock HM, Pitt MA, Martin-Hirsch PL, Martin F. A self-learning fuzzy classifier with feature selection for intelligent interrogation of mid-IR spectroscopy data derived from different categories of exfoliative cervical cytology. Int J Comput Intell Res 2008, 4:392-401. Special Issue on the Future of Fuzzy Systems Research.
    • (2008) Int J Comput Intell Res , vol.4 , pp. 392-401
    • Kelly, J.1    Angelov, P.2    Walsh, M.J.3    Pollock, H.M.4    Pitt, M.A.5    Martin-Hirsch, P.L.6    Martin, F.7
  • 61
    • 0042168791 scopus 로고    scopus 로고
    • Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissues
    • Futschik M, Reeve A, Kasabov N. Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissues. Artif Intell Med 2003, 28:165-189.
    • (2003) Artif Intell Med , vol.28 , pp. 165-189
    • Futschik, M.1    Reeve, A.2    Kasabov, N.3
  • 63
    • 79959462360 scopus 로고    scopus 로고
    • On utilizing self-organizing fuzzy neural networks for financial forecasts in the NN5 forecasting competition
    • Barcelona: IEEE
    • Coyle D, Prasad G, McGinnity TM. On utilizing self-organizing fuzzy neural networks for financial forecasts in the NN5 forecasting competition. In: International Joint Conference on Neural Networks (IJCNN). Barcelona: IEEE; 2010, 1-8.
    • (2010) International Joint Conference on Neural Networks (IJCNN) , pp. 1-8
    • Coyle, D.1    Prasad, G.2    McGinnity, T.M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.