Data-driven evolving fuzzy systems using eTS and FLEXFIS: comparative analysis

References

• Angelov , P. 2002 . Evolving Rule-Based Models , Berlin : Springer Verlag .
   Google Scholar
• Angelov, P. and Buswell, R., 2001. Evolving rule-based models: a tool for intelligent adaptation. In Proceedings of the 9th IFSA World Congress, Vancouver, BC, Canada, pp. 1062–1067
   Google Scholar
• Angelov , P. and Filev , D. 2004a . An approach to online identification of Takagi–Sugeno fuzzy models . IEEE Transactions on System, Man and Cybernetics, Part B , 34 : 484 – 498 .
   PubMed Web of Science ®Google Scholar
• Angelov , P. and Filev , D. 2004b . Flexible models with evolving structure . International Journal of Intelligent Systems , 19 : 327 – 340 .
   Web of Science ®Google Scholar
• Angelov, P. and Filev, D., 2005. Simpl_eTS: a simplified method for learning evolving Takagi–Sugeno fuzzy models. In Proceedings of the Proceedings of FUZZ-IEEE 2005, Reno, Nevada, USA, pp. 1068–1073
   Google Scholar
• Angelov, P. and Zhou, X.W., 2006. Evolving fuzzy systems from data streams in real-time. In Proceedings of the International Symposium on Evolving Fuzzy Systems (best runner-up paper award) Ambleside, Lake District, UK, pp. 29–35
   Google Scholar
• Angelov, P., Xydeas, C. and Filev, D., 2004. Online identification of MIMO evolving Takagi–Sugeno fuzzy models. In Proceedings of the Proceedings of IJCNN-FUZZ-IEEE 2004, Budapest, Hungary, pp. 55–60
   Google Scholar
• Angelov , P. , Giglio , V. , Guardiola , C. , Lughofer , E. and Luján , J. 2006 . An approach to model-based fault detection in industrial measurement systems with application to engine test benches . Measurement Science and Technology , 17 : 1809 – 1818 .
   Google Scholar
• Babuska, R., 1999. Fuzzy modeling and identification toolbox – for use with MATLAB
   Google Scholar
• Ben-David, S., Gehrke, J. and Kifer, D., 2004. Detecting change in data streams. In Proceedings of the Proceedings VLDB-04
   Google Scholar
• Carline, D., Angelov, P. and Clifford, R., 2005. Agile collaborative autonomous agents for robust underwater classification scenarios. In Proceedings of the Underwater Defense Technology. Amsterdam, the Netherlands
   Google Scholar
• Carpenter , G.A. and Grossberg , S. 1995 . “ Adaptive resonance theory (ART) ” . In The Handbook of Brain Theory and Neural Networks , Edited by: Arbib , M.A. 79 – 82 . Cambridge, MA : MIT Press .
   Google Scholar
• Chiu , S. 1994 . Fuzzy model identification based on cluster estimation . Journal of Intelligent and Fuzzy Systems , 2 : 267 – 278 .
   Web of Science ®Google Scholar
• Duda , R. , Hart , P. and Stork , D. 2000 . Pattern Classification – Second Edition , Chichester : Wiley-Interscience .
   Google Scholar
• Espinosa , J. and Vandewalle , J. 2000 . Constructing fuzzy models with linguistic intergrity from numerical data – AFRELI algorithm . IEEE Transactions on Fuzzy Systems , 8 : 591 – 600 .
   Google Scholar
• Gray , R. 1984 . Vector quantization . IEEE ASSP Magazine , : 4 – 29 .
   Google Scholar
• Hastie , T. , Tibshirani , R. and Friedman , J. 2001 . The Elements of Statistical Learning: Data Mining, Inference and Prediction , New York, Berlin, Heidelberg : Springer Verlag .
   Google Scholar
• Himmelbauer, J. and Drobics, M., 2004. Regularized numerical optimization of fuzzy rule bases. In Proceedings of the Proceedings of FUZZ-IEEE 2004. Budapest, Hungary
   Google Scholar
• Jang , J.S. 1993 . ANFIS: adaptive-network-based fuzzy inference systems . IEEE Transactions on System, Man and Cybernetics , 23 : 665 – 685 .
   Web of Science ®Google Scholar
• Jang , J.S. and Sun , C.T. 1993 . Functional equivalence between radial basis function networks and fuzzy inference systems . IEEE Transactions on Neural Networks , 4 : 156 – 159 .
   PubMed Web of Science ®Google Scholar
• Kasabov , N.K. and Song , Q. 2002 . DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction . IEEE Transactions on Fuzzy Systems , 10 : 144 – 154 .
   Web of Science ®Google Scholar
• Ljung , L. 1999 . System Identification: Theory for the User , Upper Saddle River, New Jersey : Prentice Hall PTR, Prentic Hall Inc .
   Google Scholar
• Lughofer, E. and Klement, E., 2004. Premise parameter estimation and adaptation in fuzzy systems with open-loop clustering methods. In Proceedings of the Proceedings of FUZZ-IEEE 2004. Budapest, Hungary
   Google Scholar
• Lughofer, E. and Klement, E., 2005. FLEXFIS: a variant for incremental learning of Takagi–Sugeno fuzzy systems. In Proceedings of the Proceedings of FUZZ-IEEE 2005. Reno, Nevada, USA pp. 915–920
   Google Scholar
• Lughofer , E. and Bodenhofer , U. 2006 . “ Incremental learning of fuzzy basis function networks with a modified version of vector quantization ” . In Proceedings of the Proceedings of IPMU 2006 Vol. 1 , 56 – 63 . Paris, France
   Google Scholar
• Lughofer, E., Hüllermeier, E. and Klement, E., 2005. Improving the interpretability of data-driven evolving fuzzy systems. In Proceedings of the Proceedings of EUSFLAT 2005. Barcelona, Spain, 28–33
   Google Scholar
• Mouzouris , G. and Mendel , J. 1997 . A singular-value-QR decomposition based method for training fuzzy logic systems in uncertain environments . Journal of Intelligent and Fuzzy Systems , 5 : 367 – 374 .
   Google Scholar
• Qin , S. , Li , W. and Yue , H. 2000 . Recursive PCA for adaptive process monitoring . Journal of Process Control , 10 : 471 – 486 .
   Google Scholar
• Ramos, J.V. and Dourado, A., 2004. Online interpretability by fuzzy rulebase simplification and reduction, In Proceedings of the European Symposium on Intelligent Technologies, EUNITE 2004. Aachen, Germany
   Google Scholar
• Reoullas , M. , Chiao , S.Y. , Angelov , P. and Xydeas , C. 2006 . Advanced in classification of EEG signals using an evolving fuzzy classifier and dependant multiple HMM . International Journal Computers in Biology and Medicine , 36 : 1064 – 1083 .
   Google Scholar
• Setnes , M. 2003 . “ Simplification and reduction of fuzzy rules ” . In Interpretability Issues in Fuzzy Modeling , Edited by: Casillas , J. , Cordón , O. , Herrera , F. and Magdalena , L. 278 – 302 . Berlin : Springer . 128 of Studies in Fuzziness and Soft Computing
   Google Scholar
• Setnes , M. , Babuska , R. , Kaymak , U. and Lemke , H. 1998 . Similarity measures in fuzzy rule base simplification . IEEE Transactions SMC-B , 28 : 376 – 386 .
   PubMed Web of Science ®Google Scholar
• Tetko , I. , Livingstone , D. and Luik , A. 1995 . Neural network studies. 1. Comparison of overfitting and overtraining . Journal of Chemical Information and Computer Sciences , 35 : 826 – 833 .
   Google Scholar
• Tsymbal, A., 2004. The problem of concept drift: definitions and related work, Technical report TCD-CS-2004-15, Department of Computer Science, Trinity College Dublin, Ireland
   Google Scholar
• Wang , L. and Mendel , J. 1992 . Fuzzy basis functions, universal approximation and orthogonal least-squares learning . IEEE Transactions Neural Networks , 3 : 807 – 814 .
   PubMed Web of Science ®Google Scholar
• Yager, R. and Filev, D., 1993. Learning of fuzzy rules by mountain clustering, In Proceedings of SPIE Conference on Application of Fuzzy Logic Technology. Boston, MA, USA, 246–254
   Google Scholar
• Yager, R. and Filev, D., 1993. Generation of fuzzy rules by mountain clustering. Technical report MII-1318R, Machine Intelligence Institute, Iona College, New Rochelle, NY 10801
   Google Scholar
• Yen , J. and Wang , L. 1999 . Simplifying fuzzy rule-based models using orthogonal transformation methods . IEEE Transactions on Systems, Man and Cybernetics – Part B: Cybernetics , 29 : 13 – 24 .
   PubMed Web of Science ®Google Scholar