Granular computing as a framework of system modeling

Journal of Control, Automation and Electrical Systems

Volumn 24, Issue 1-2, 2013, Pages 81-86

  Pedrycz, Witold ^a,b,c  

^a UNIVERSITY OF ALBERTA   (Canada)

^b KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

^c SYSTEMS RESEARCH INSTITUTE   (Poland)

Author keywords

Computational Intelligence; Granular Computing; Granular model; Information granules; Rule based models

Indexed keywords

FUNCTIONAL BLOCK; GRANULAR MODELS; KEY CHARACTERISTICS; RULE-BASED MODELS; SHADOWED SETS; SYSTEM MODELING;

ARTIFICIAL INTELLIGENCE; FUZZY SETS; INFORMATION GRANULES;

GRANULAR COMPUTING;

EID: 84879642774     PISSN: 21953880     EISSN: 21953899     Source Type: Journal    
DOI: 10.1007/s40313-013-0010-9     Document Type: Article

References (25)

1. Bargiela, A.; Pedrycz, W. (2003). Granular computing: An introduction. Dordrecht: Kluwer Academic Publishers.
2. Bargiela, A.; Pedrycz, W. (2008). Toward a theory of granular computing for human-centered information processing. IEEE Transactions on Fuzzy Systems, 16(2), 320-330.
3. Bargiela, A.; Pedrycz, W. (2009). Human-centric information processing through granular modelling. Heidelberg: Springer-Verlag.
4. Engelbrecht, A. P. (2005). Fundamentals of computational swarm intelligence. London, UK: Wiley.
5. Goldberg, D. E. (1989). Genetic algorithms in search, pptimization, and machine learning. Reading, MA: Addison Wesley.
6. Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Upper Saddle River, NJ: Prentice Hall.
7. Hirota, K. (1981). Concepts of probabilistic sets. Fuzzy Sets and Systems, 5(1), 31-46.
8. Hirota, K.; Pedrycz, W. (1984). Characterization of fuzzy clustering algorithms in terms of entropy of probabilistic sets. Pattern Recognition Letters, 2(4), 213-216.
9. Liu, X.; Pedrycz, W. (2009). Axiomatic fuzzy set theory and its applications. Berlin: Springer-Verlag.
10. Moore, R. (1966). Interval analysis. Englewood Cliffs, NJ: Prentice-Hall.
11. Pawlak, Z. (1982). Rough sets. International Journal of Computing and Information Sciences, 11, 341-356.
12. Pawlak, Z. (1985). Rough sets and fuzzy sets. Fuzzy Sets and Systems, 17(1), 99-102.
13. Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishers.
14. Pedrycz, W. (1998). Shadowed sets: Representing and processing fuzzy sets. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 28, 103-109.
15. Pedrycz, W. (1999). Shadowed sets: Bridging fuzzy and rough sets. In S. K. Pal; A. Skowron (Eds.), Rough fuzzy hybridization. A new trend in decision-making (pp. 179-199). Singapore: Springer Verlag.
16. Pedrycz, W. (2005). Interpretation of clusters in the framework of shadowed sets. Pattern Recognition Letters, 26(15), 2439-2449.
17. Pedrycz, W. (2012). Allocation of information granularity in optimization and decision making models: Towards building the foundations of Granular Computing. European Journal of Operational Research, (in press).
18. Pedrycz, W. (2013). Analysis and design of intelligent systems: A framework of granular computing. Boca Raton, FL: Taylor; Francis CRC Press.
19. Pedrycz, W.; Gomide, F. (1998). An introduction to fuzzy sets: Analysis and design. Cambridge, MA: MIT Press.
20. Pedrycz, W.; Gomide, F. (2007). Fuzzy systems engineering: Toward human-centric computing. Hoboken, NJ: John Wiley.
21. Pedrycz, W.; Skowron, A.; Kreinovich, V. (2008). Handbook of granular computing (pp. 347-373). Chichester: John Wiley; Sons.
22. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
23. Zadeh, L. A. (1997). Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90, 111-117.
24. Zadeh, L. A. (1999). From computing with numbers to computing with words-from manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuits and Systems, 45, 105-119.
25. Zadeh, L. A. (2005). Toward a generalized theory of uncertainty (GTU) - An outline. Information Sciences, 172, 1-40.