Adaptive inferential sensors based on evolving fuzzy models

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 40, Issue 2, 2010, Pages 529-539

  Angelov, Plamen ^a   Kordon, Arthur ^b  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b DOW CHEMICAL COMPANY   (United States)

Author keywords

Concept shift in data streams; Evolving fuzzy systems; Fuzzy rule aging; Inferential sensors; Learning and adaptation; Takagi Sugeno (TS) fuzzy models

Indexed keywords

CONCEPT SHIFT IN DATA STREAMS; DATA STREAM; INFERENTIAL SENSORS; LEARNING AND ADAPTATION; TAKAGI-SUGENO FUZZY MODELS;

CHEMICAL INDUSTRY; CHEMICAL PROPERTIES; COST REDUCTION; DATA COMMUNICATION SYSTEMS; FORECASTING; FUZZY SYSTEMS; MAINTAINABILITY; REAL VARIABLES;

SENSORS;

EID: 77949774043     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2009.2028315     Document Type: Article

References (31)

1. L. Fortuna, S. Graziani, A. Rizzo, and M. G. Xibilia, Soft Sensors for Monitoring and Control of Industrial Processes. London, U.K.: Springer-Verlag, 2007.
2. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction. Heidelberg, Germany: Springer-Verlag, 2001.
3. R. Rallo, J. Ferre-Gine, A. Arenas, and F. Giralt, "Neural virtual sensor for the inferential prediction of product quality from process variables," Comput. Chem. Eng., vol.26, no.12, pp. 1735-1754, Dec. 2002.
4. R. Klinkenberg and T. Joachims, "Detection concept drift with support vector machines," in Proc. 7th ICML, 2000, pp. 487-494.
5. A. Kordon and G. Smits, "Soft sensor development using genetic programming," in Proc. GECCO, San Francisco, CA, 2001, pp. 1346-1351.
6. F. Hopner and F. Klawonn, "Obtaining interpretable fuzzy models from fuzzy clustering and fuzzy regression," in Proc. 4th Int. Conf. Knowl.- Based Intell. Eng. Syst., Brighton, U.K., 2000, pp. 162-165.
7. P. Angelov, Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems. Berlin, Germany: Springer-Verlag, 2002.
8. G. Widmer and M. Kubat, "Learning in the presence of concept drift and hidden contexts," Mach. Learn., vol.23, no.1, pp. 69-101, Apr. 1996.
9. P. Angelov and D. Filev, "An approach to on-line identification of evolving Takagi-Sugeno models," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol.34, no.1, pp. 484-498, Feb. 2004.
10. P. Angelov and X. Zhou, "Evolving fuzzy systems from data streams in real-time," in Proc. Int. Symp. Evolving Fuzzy Syst., Ambleside, U.K., Sep. 7-9, 2006, pp. 29-35.
11. J. Macias, P. Angelov, and X. Zhou, "Predicting quality of the crude oil distillation using evolving Takagi-Sugeno fuzzy models," in Proc. Int. Symp.EvolvingFuzzy Syst., Ambleside,U.K.,Sep. 7-9, 2006, pp. 201-207.
12. P. Angelov and X. Zhou, "On line learning fuzzy rule-based system structure from data streams," in Proc. IEEE Int. Conf. Fuzzy Syst. Within IEEE World Congr. Comput. Intell., Hong Kong, Jun. 1-6, 2008, pp. 915-922.
13. A. Kordon, G. Smits, A. Kalos, and E. Jordaan, "Robust soft sensor development using GP," in Nature-Inspired Methods in Chemometrics, R. Leardi, Ed. Amsterdam, The Netherlands: Elsevier, 2003, pp. 69-108.
14. P. Angelov and R. Buswell, "Identification of evolving rule-based models," IEEE Trans. Fuzzy Syst., vol.10, no.5, pp. 667-677, Oct. 2002.
15. K. J. Astroem and B. Wittenmark, Adaptive Control. Reading, MA: Addison-Wesley, 1989.
16. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its application to modeling and control," IEEE Trans. Syst., Man, Cybern., vol.SMC-15, no.1, pp. 116-132, Jan./Feb. 1985.
17. L.-X. Wang, "Fuzzy systems are universal approximators," in Proc. FUZZ-IEEE, San Diego, CA, 1992, pp. 1163-1170.
18. R. Yager and D. Filev, Essentials of Fuzzy Modelling and Control. New York: Wiley, 1994.
19. P. Angelov and D. Filev, "Simpl eTS: A simplified method for learning evolving Takagi-Sugeno fuzzy models," in Proc. IEEE Int. Conf. Fuzzy Syst., Reno, NV, May 22-25, 2005, pp. 1068-1073.
20. P. Domingos and G. Hulten, "Catching up with the data: Research issues in mining data streams," in Proc. Workshop Res. Issues Data Mining Knowl. Discov., Santa Barbara, CA, 2001.
21. E. Y. Nagai and L. V. R. Arruda, "Soft sensor based on fuzzy model identification," inProc. 16th IFACWorld Congr., Prague, Czech Republic, 2005.
22. W. Yan, H. Shao, and X. Wang, "Soft sensing modeling based on support vector machine and Bayesian model selection," Comput. Chem. Eng., vol.28, no.8, pp. 1489-1498, Jul. 2004.
23. J. Liu, "On-line soft sensor for polyethylene process with multiple production grades," in Proc. 16th IFAC World Congr., Prague, Czech Republic, 2005.
24. P. Angelov and M. Everett, "EvoMap: Evolving mapping- Implementation of evolving clustering on FPGA," Lancaster Univ., Lancaster, U.K., 2005.
25. X. Zhou and P. Angelov, "An approach to autonomous self-localization of a mobile robot in completely unknown environment using evolving fuzzy rule-based classifier," in Proc. 1st IEEE Int. Conf. Comput. Intell. Appl. Defense Security, Honolulu, HI, Apr. 1-5, 2007, pp. 131-138.
26. A. Ferreyra and J. J. Rubio, "A new on-line self-constructing neural fuzzy network," in Proc. 45th IEEE Conf. Decision Control, Dec. 13-15, 2006, pp. 3003-3009.
27. P. S. Lee and A. L. Dexter, "A fuzzy sensor for measuring the mixed air temperature in air handling units," Measurement, vol.37, no.1, pp. 83-93, Jan. 2005.
28. M. J. Arauzo-Bravo, J. M. Cano-Izquierdo, E. Gomez-Sanchez, M. J. Lopez-Nieto, Y. A. Dimitriadis, and J. Lopez-Coronado, "Automatization of a penicillin production process with soft sensors and an adaptive controller based on neuro-fuzzy systems," Control Eng. Pract., vol.12, no.9, pp. 1073-1090, Sep. 2004.
29. R. M. French, "Catastrophic forgetting in connectionist networks," Trends Cogn. Sci., vol.3, no.4, pp. 128-135, Apr. 1999.
30. J. Kelly, P. Angelov, M. J. Walsh, H. M. Pollock, M. A. Pitt, P. L. Martin-Hirsch, and F. Martin, "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.-Special Issue on the Future of Fuzzy Systems Research, vol.4, no.4, pp. 392-401, Dec. 2008, invited paper.
31. N. K. Kasabov and Q. Song, "DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction," IEEE Trans. Fuzzy Syst., vol.10, no.2, pp. 144-154, Apr. 2002.