Evolving Takagi-Sugeno Fuzzy Systems from Streaming Data (eTS+)

Evolving Intelligent Systems: Methodology and Applications

Volumn , Issue , 2010, Pages 21-50

  Angelov, Plamen ^a  

^a LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

Evolving Takagi Sugeno fuzzy systems from streaming data (eTS+); FwRLS method and parameter identification; Nonlinear regression and time series prediction using eTS+

Indexed keywords

EID: 84886838236     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470569962.ch2     Document Type: Chapter

References (45)

1. Angelov, P., R. Buswell (2001). "Evolving Rule-Based Models:ATool for Intelligent Adaptation."Proc. 9th IFSA World Congress, Vancouver, BC, Canada, July 25-28, 2001, pp. 1062-1067.
2. Angelov, P. (2002). Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems. Heidelberg, New York; Springer-Verlag, ISBN 3-7908-1457-1.
3. Angelov, P., R. Buswell (2003). "Automatic Generation of Fuzzy Rule-Based Models from Data by Genetic Algorithms," Information Sciences, Vol. 150, No. 1/2, pp. 17-31.
4. Angelov, P., D. Filev (2003). "On-line Design of Takagi-Sugeno Models," In: Lecture Notes in Computer Sci., Vol. 2715, Fuzzy Sets and Systems, IFSA2003 ( T. BilgiÇ, B. De Baets, O. Kaynak, eds.), pp. 576-584.
5. Angelov, P. (2004). "An Approach for Fuzzy Rule-Based Adaptation Using On-line Clustering," Int. Journal of Approximate Reasoning, Vol. 35, No. 3, pp. 275-289.
6. Angelov, P. P. (2004b). "A Fuzzy Controller with Evolving Structure," Information Sciences, Vol. 161, pp. 21-35, ISSN 0020-0255.
7. Angelov, P., D. Filev (2004). "An Approach to On-line Identification of Takagi-Sugeno Fuzzy Models." IEEE Trans. on System, Man, and Cybernetics, Part B-Cybernetics, Vol. 34, No. 1, pp. 484-498, ISSN 1094-6977.
8. Angelov, P., C. Xydeas, D. Filev (2004a). "On-line Identification of MIMO Evolving Takagi- Sugeno Fuzzy Models." Proc. International Joint Conference on Neural Networks and International Conference on Fuzzy Systems, IJCNN-FUZZ-IEEE, Budapest, Hungary, July 25-29, 2004, pp. 55-60, ISBN 0-7803-8354-0.
9. Angelov, P., J. Victor, A. Dourado, D. Filev (2004b). On-line Evolution of Takagi-Sugeno Fuzzy Models. Proc. 2nd IFAC Workshop on Advanced Fuzzy/Neural Control, Oulu, Finland, September 16-17, 2004, pp. 67-72.
10. Angelov, P., D. Filev (2005). "Simpl_eTS: A Simplified Method for Learning Evolving Takagi- Sugeno Fuzzy Models." 2005 IEEE Int. Conference on Fuzzy Systems, FUZZ-IEEE, Reno, Nevada, May 22-25, 2005, pp. 1068-1073, ISSN 0-7803-9158-6/05.
11. Angelov, P. (2006). Machine Learning (Collaborative Systems) Patent ( WO2008053161, priority date-November 1, 2006; international filing date October 23, 2007).
12. Angelov, P., X. Zhou (2006). "Evolving Fuzzy Systems from Data Streams in Real-Time." Proc. 2006 Int. Symposium on Evolving Fuzzy Syst., Ambleside, UK, September 7-9, 2006, IEEE Press, pp. 29-35, ISBN 0-7803-9719-3.
13. Angelov, P., X. Zhou, F. Klawonn (2007). "Evolving Fuzzy Rule-Based Classifiers." 2007 IEEE Int. Conference on Computational Intelligence Applications for Signal and Image Processing, Honolulu, Hawaii, April 1-5, 2007, IEEE Press, pp. 220-225.
14. Angelov, P., E. Lughofer (2008). "A Comparative Study of Two Approaches for Data-Driven Design of Evolving Fuzzy Systems: eTS and FLEXFIS." Int. Journal on General Systems, Vol. 37, Issue 1, February 2008, pp. 45-67.
15. Angelov, P., X. Zhou (2008a). "On Line Learning Fuzzy Rule-based System Structure from Data Streams." Proc. World Congress on Computational Intelligence, WCCI-2008, Hong Kong, June 1-6, 2008, IEEE Press, pp. 915-922, ISBN 978-1-4244-1821-3/08.
16. Angelov, P., X. Zhou (2008b). "Evolving Fuzzy Rule-Based Classifiers from Data Streams." IEEE Transactions on Fuzzy Systems, Vol. 16 (6), Special Issue on Evolving Fuzzy Systems,pp. 1462-1475.
17. Angelov, P., R. Ramezani, X. Zhou (2008a). "Autonomous Novelty Detection and Object Tracking in Video Streams Using Evolving Clustering and Takagi-Sugeno Type Neuro-Fuzzy System." World Congress on Computational Intelligence, Hong Kong, June 1-6, 2008, pp. 1457-1464, ISBN 978-1-4244-1821-3/08.
18. Angelov, P., A. Kordon, X. Zhou (2008b). "Evolving Fuzzy Inferential Sensors for Process Industry." Proc. 3rd International Workshop on Genetic and Evolving Fuzzy Systems, Witten- Bomerholz, Germany, March 4-7, 2008, IEEE Press, pp. 41-46, ISBN 978-1-4244-1613-4.
19. Angelov, P., C. D. Bocaniala, C. Xydeas, C. Pattchet, D. Ansell, M. Everett, G. Leng (2008c). "A Passive Approach to Autonomous Collision Detection and Avoidance in Uninhabited Aerial Systems" Proc. 2008 EUROSIM, Emanuel College, Cambridge, UK, April 1-3, 2008, pp. 64-69.
20. Astrom, K. J., B. Wittenmark (1989). Adaptive Control. Reading, MA: Addison-Wesley. Asuncion, A., D. Newman (2007). "UCI Machine Learning Repository'', http://mlearn.ics.uci. edu/MLRepository.html.
21. Chiu, S. (1994). "Fuzzy Model Identification Based on Cluster Estimation," Journal of Intelligent and Fuzzy Systems, Vol. 2 (3), pp. 267-278.
22. Domingos, P., G. Hulten (2001). "Catching Up with the Data: Research Issues in Mining Data Streams." Workshop on Research Issues in Data Mining and Knowledge Discovery, Santa Barbara, CA, 2001.
23. Duda, R. O., P. E. Hart, D. G. Stork (2000). Pattern Classification-Second Edition. Chichester, West Sussex, England: Wiley-Interscience. EC (2007). "NMP-2008-3.2-2-Self-Learning Production Systems." Work Programme on Nanosciences, Nanotechnologies, Materials and New Production Technologies, available online at ftp://ard.huji.ac.il/pub/251/NMP_Fiche_small_2008_02.pdf.
24. EC (2007). "NMP-2008-3.2-2-Self-Learning Production Systems." Work Programme on Nanosciences, Nanotechnologies, Materials and New Production Technologies, available online at ftp://ard.huji.ac.il/pub/251/NMP_Fiche_small_2008_02.pdf
25. Elgammal, A., R. Suraiswami, D. Harwood, and L. Davis (2002). "Background and Foreground Modeling Using Non-parametric Kernel Density Estimation for Visual Surveillance KDE," Proc. 2002 IEEE, Vol. 90, No. 7, pp. 1151-1163.
26. Fortuna, L., S. Graziani, A. Rizzo, M. G. Xibilia (2007). "Soft Sensor for Monitoring andControl of Industrial Processes. In Advances in Industrial Control Series ( M. J. Grimble, M.A. Johnson, eds.). Berlin, Springer-Verlag.
27. Hastie, T., R. Tibshirani, and J. Friedman (2001). The Elements of Statistical Learning: Data Mining, Inference and Prediction. Heidelberg: Springer-Verlag.
28. Jang, J. S.R. (1993). "ANFIS: Adaptive Network-Based Fuzzy Inference Systems." IEEE Trans. on SMC, Vol. 23, No. 3, pp. 665-685.
29. Kalman, R. (1960). "A New Approach to Linear Filtering and Prediction Problems." Trans. of the ASME-Journal of Basic Engineering, Vol. 82, pp. 35-45.
30. Kasabov, N., Q. Song (2002). "DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and Its Application for Time-Series Prediction." IEEE Trans. on Fuzzy Systems, Vol. 10, No. 2, pp. 144-154.
31. Kordon, A., G. Smits, A. Kalos, and E. Jordaan (2003). "Robust Soft Sensor Development Using Genetic Programming." In Nature-Inspired Methods in Chemometrics (R. Leardi, ed.). Amsterdam: Elsevier, pp. 69-108.
32. Li,W., H. H. Yue, S. Valle-Cervantes, and S. J. Qin (2000). "Recursive PCA for Adaptive Process Monitoring." Journal of Process Control, Vol. 10 (5), pp. 471-486.
33. Ljung, L. (1988). System Identification, Theory for the User. Englewood Cliffs, NJ: Prentice-Hall.
34. Lughofer, E., E. Klement (2005). "FLEXFIS: A Variant for Incremental Learning of Takagi-Sugeno Fuzzy Systems," Proc. FUZZ-IEEE 2005, Reno, Nevada, pp. 915-920.
35. Patchett, C., V. Sastri (2007). "Decision Architectures for Uninhabited Autonomous AerialSystems." EuroSim 2007, Ljubljana, Slovenia, September 9-13, 2007.
36. Qin, S. J., H.Yue, and R. Dunia (1997). "Self-Validating Inferential Sensors with Application to Air Emission Monitoring," Industrial Engineering Chemistry Research, Vol. 36, pp.1675-1685.
37. Roubos, J., M. Setnes, J. Abonyi (2003). "Learning Fuzzy Classification Rules from Data."Information Sciences, Vol. 150, pp. 77-93.
38. Silva, L., F. Gomide, R. R. Yager (2005). "Participatory Learning in Fuzzy Clustering." Proc. 24th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE, Reno, Nevada, May 22-25, 2005, pp. 857-861, ISSN 0-7803-9158-6/05.
39. Smits, G., A. Kordon, E. Jordaan, C. Vladislavleva, M. Kotanchek (2006). "Variable Selection in Industrial Data Sets Using Pareto Genetic Programming," In Genetic Programming Theory and Practice III. (T. Yu, R. Riolo, B. Worzel, eds.). New York: Springer, pp. 79-92.
40. Sugeno, M., K. T. Kang (1988). "Structure Identification of Fuzzy Model." Fuzzy Sets and Systems, Vol. 28, No. 1, pp. 15-23.
41. Takagi, T., M. Sugeno (1985). "Fuzzy Identification of Systems and Its Applications to Modeling and Control." IEEE Trans. on Systems, Man and Cybernetics, Vol. 15, No. 1, pp. 116-132.
42. Wang, L., J. Mendel (1992). "Fuzzy Basis Functions, Universal Approximation and Orthogonal Least-Squares Learning." IEEE Trans. on Neural Networks, Vol. 3, No. 5, pp. 807-814.
43. Yager, R., D. Filev (1994). "Approximate Clustering via the Mountain Method." IEEE Trans.on Systems and Cybernetics, Vol. 24, No. 8, pp. 1279-1284.
44. Yen, J., L.Wang, C.W. Gillespie (1998). "Improving the Interpretability of TSK Fuzzy Models by Combining Global and Local Learning," IEEE Trans. Fuzzy Syst., Vol. 6, pp. 530-537.
45. Zadeh L. A. (1975). "The Concept of a Linguistic Variable and Its Application to Approximate Reasoning: Parts I, II, and III," Information Sciences, Vol. 8, pp. 199-249, 301-357;vol. 9, pp. 43-80.