Interval approach for evolving granular system modeling

Learning in Non-Stationary Environments: Methods and Applications

Volumn 9781441980205, Issue , 2013, Pages 271-300

  Leite, Daniel ^a   Costa, Pyramo ^b   Gomide, Fernando ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

^b PONTIFICAL CATHOLIC UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

GRANULAR COMPUTING;

CHANGE-OVER TIME; DATA STREAM; EVOLVING MODELS; GRANULAR SYSTEM; MODELING APPROACH; NONSTATIONARY DATA; PHYSICAL SYSTEMS; SYSTEM CHANGE; SYSTEM MODELS; TIME-VARYING SYSTEMS;

TIME VARYING SYSTEMS;

EID: 84930328686     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-4419-8020-5_11     Document Type: Chapter

References (34)

1. Kreinovich, V.: Interval computations as an important part of granular computing: an introduction. In: Pedrycz, W.; Skowron, A.; Kreinovich, V. (Eds.). Handbook of Granular Computing, pp. 1-31 (2008)
2. Pedrycz, W.: Evolvable fuzzy systems: some insights and challenges. Evolving Systems, 1(2), pp. 73-82 (2010)
3. Bargiela A., Pedrycz, W.: Granulation of temporal data: a global view on time series. Int. Conf. of the North American Fuzzy Information Processing Society, pp. 191-196 (2003)
4. Angelov, P., Filev, D.: An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics, 34(1), pp. 484-498 (2004)
5. Leite, D., Gomide, F.: Evolving linguistic fuzzy models from data streams. To appear in: Trillas, E.; Bonissone, P.; Magdalena, L.; Kacprycz, J. (Eds.). Studies in Fuzziness and Soft Computing: A Homage to Abe Mamdani, Springer (2011)
6. Leite, D., Costa Jr. P., Gomide, F.: Evolving granular neural network for semi-supervised data stream classification. International Joint Conference on Neural Networks, pp. 1-8 (2010)
7. Leite, D.; Costa Jr., P.; Gomide, F.: Interval-based evolving modeling. IEEE Symposium Series on Computational Intelligence - Workshop ESDIS, pp. 1-8 (2009)
8. Leite, D.; Costa Jr., P.; Gomide, F: Granular approach for evolving system modeling. In: E. Hullermeier, R. Kruse, and F. Hoffmann (Eds.). Lecture Notes in Artificial Intelligence, 6178, pp. 340-349, Springer (2010)
9. Yao, Y. Y.: Interpreting concept learning in cognitive informatics and granular computing. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 39(4), pp. 855-866 (2009)
10. Yao, Y. Y.: Human-inspired granular computing. In: J. T. Yao (Ed.), Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computing (2010)
11. Yao, Y. Y.: The art of granular computing. International Conference on Rough Sets and Emerging Intelligent Systems Paradigms, LNAI 4585, pp. 101-112 (2007)
12. Zadeh, L. A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), pp. 111-127 (1997)
13. Pedrycz, W., Skowron, A., Kreinovich, V. (Eds.): Handbook of Granular Computing. Wiley-Interscience, Hoboken, New Jersey (2008)
14. Bargiela A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers - Boston, Dordrecht, London (2003)
15. Jaulin, L., Keiffer, M., Didrit, O., Walter, E.: Applied Interval Analysis. Springer-Verlag, London (2001)
16. Shafer, J. L.: Analysis of Incomplete Multivariate Data. Chapman and Hall, London (1997)
17. Little, R. J. A., Rubin, D. B.: Statistical Analysis with Missing Data. 2nd edition, Wiley-Interscience, Hoboken, New Jersey (2002)
18. Ozawa, S.; Pang, S.; Kasabov, N.: Incremental learning of chunk data for online pattern classification systems. IEEE Trans. on Neural Networks, 19(6), pp. 1061-1074 (2008)
19. Last, M.: Online classification of nonstationary data streams. Intelligent data analysis, vol. 6(2), pp. 129-147 (2002)
20. Yager, R.: Measures of specificity over continuous spaces under similarity relations. Fuzzy Sets and Systems, 159(17), pp. 2193-2210 (2008)
21. Hansen, E. R.; Walster, G. W.: Global Optimization using Interval Analysis. Marcel Dekker, 2nd edition, New York - Basel (2004)
22. Moore, R. E.: Interval Analysis. Prentice Hall, Englewood Cliffs, N. J. (1966)
23. Moore, R. E.: Methods and Applications of Interval Analysis. SIAM, Philadelphia (1979)
24. Kearfott, R. B.; Kreinovich, V.: Applications of Interval Computations. Kluwer Academic Publishers (1996)
25. Neumaier, A.: Interval Methods for Systems of Equations. Cambridge University Press, Cambridge (1990)
26. Hickey, T., Ju, Q., Van Emden, M. H.: Interval arithmetic: from principles to implementation. Journal of the ACM, textbf 48(5), pp. 1038-1068 (2001)
27. Moore, R. E., Kearfott, R. B., Cloud, M. J.: Introduction to Interval Analysis. SIAM, Philadelphia (2009)
28. Petkovic, M. S., Petkovic, L, D.: Complex Interval Arithmetic and Its Applications. Wiley - VCH, Germany (1998)
29. Hahn, G. J., Meeker, W. Q.: Statistical Intervals: A Guide for Practitioners. Wiley, USA (1991)
30. Moore, R. E.; Lodwick, W.: Interval analysis and fuzzy set theory. Fuzzy Sets and Systems, 135(1), pp. 5-9 (2003)
31. Young, P. C.: Recursive Estimation and Time-Series Analysis: An Introduction. Springer-Verlag, Berlin, (1984)
32. Ballini, R., Mendonca, A., Gomide, F.: Evolving fuzzy modeling of sovereign bonds. The Journal of Financial Decision Making, special issue: The Fuzzy Logic in the Financial Uncertainty, 5 (2) (2009)
33. Astrom, K. J.; Wittenmark, B.: Adaptive Control. Addison-Wesley Longman Publishing Co., Inc., Boston, USA, 2 edition, (1994)
34. Johnson, C. R.: Lectures on Adaptive Parameter Estimation. Prentice-Hall, Inc., Upper Saddle River, USA (1988)