A weight adaptation method for fuzzy cognitive map learning

Soft Computing

Volumn 9, Issue 11, 2005, Pages 846-857

  Papageorgiou, Elpiniki I ^a   Groumpos, Peter P ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

Fuzzy cognitive maps; Hebbian rule; Learning algorithms; Process control; Soft computing

Indexed keywords

EID: 29544441693     PISSN: 14327643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00500-004-0426-z     Document Type: Article

References (40)

1. Aguilar J (2002) Adaptive random fuzzy cognitive maps. In: Garijio FJ, Riquelme JC, Toro M (eds) IBERAMIA 2002, Lecture Notes in Artificial Intelligence, vol 2527. Springer, Berlin Heidelberg Newyork, pp 402-410
2. Bonissone P (1997) Soft computing: The convergence of emerging reasoning technologies. Soft Comput 1:6-18
3. Craiger JP, Goodman DF, Weiss RJ, Butler A (1996) Modeling organizational behavior with fuzzy cognitive maps. IJCIO 1:120-123
4. Dickerson J, Kosko B (1994) Fuzzy virtual worlds. AI Expert 7:25-31
5. Hassoun M (1995) Fundamentals of artificial neural networks. MIT (Bradford Book), MA
6. Hebb DO (1949) The organization of behaviour: A neuropsychological theory. Wiley, New York
7. Huerga AV (2002) A balanced differential learning algorithm in fuzzy cognitive maps. In: Proceedings of the sixteenth international workshop on qualitative reasoning 2002
8. Jain L (1997) Soft computing techniques in knowledge-based intelligent engineering systems: Approaches and applications. Studies in fuzziness and soft computing, vol 10. Springer, Berlin Heidelberg New York
9. Kang II, Lee S (2004) Using fuzzy cognitive map for the relationship management in airline service. Expert systems with applications (in press)
10. Kosko B (1986) Fuzzy cognitive maps. IJMMS 24:65-75
11. Kosko B (1997) Fuzzy engineering. Prentice-Hall, New Jersey
12. Koulouriotis DE, Diakoulakis IE, Emiris DM (2001) Learning fuzzy cognitive maps using evolution strategies: A novel schema for modeling a simulating high-level behavior. In: Proceedings of IEEE congress on evolutionary computation, vol 1. pp 364-371
13. Lee KC, Kim HS (1998) Fuzzy implications of fuzzy cognitive map with emphasis on fuzzy causal relationship and fuzzy partially causal relationship. J Fuzzy Sets Syst 3:303-313
14. Lee KC, Kwon OB (1998) A strategic planning simulation based on cognitive map knowledge and differential game. J Simul 7(5):316-327
15. Lee KC, Kin JS, Chung NH, Kwon SJ (2002) Fuzzy cognitive map approach to web-mining inference amplification. J Experts Syst Appl 22:197-211 10.1016/S0957-4174(01)00054-9
16. Lin CT, Lee CSG (1996) Neural fuzzy systems: A neuro-fuzzy synergism to intelligent systems. Prentice Hall, Upper Saddle River
17. Liu ZQ (2000) Fuzzy cognitive maps: Analysis and extension. Springer, Tokyo
18. Liu ZQ (2003) Fuzzy cognitive maps in GIS data analysis. Soft Comput 7(6):394-401. DOI 10.1007/s00500-002-0228-0
19. Liu ZQ, Satur R (1999) Contextual fuzzy cognitive map for decision support in geographical information systems. J IEEE Trans Fuzzy Syst 7:495-507 10.1109/91.797975
20. Liu ZQ, Zhang JY (2003) Interrogating the structure of fuzzy cognitive maps. Soft comput 7:148-153
21. Mateou NH, Zombanakis GA, Andreou AS (2003) Crisis management and political decision making using fuzzy cognitive maps: The Cyprus issue. In: Proceedings of 2003 international conference on intelligent agents, web technologies and internet commerce, Vienna, 12-14 February
22. Oja E (1989) Neural networks, principal components and subspaces. Int J Neural Syst 1:61-68 10.1142/S0129065789000475
23. Oja E, Karhunen J (1993) Nonlinear PCA, algorithm and applications, report A18. Helsinski University of Technology, Finland
24. Oja E, Ogawa H, Wangviwattana J (1991) Learning in nonlinear constrained Hebbian networks In: Kohonen T (Ed.) Artificial neural networks. North-Holland, Amsterdam pp. 385-390
25. Papageorgiou EI, Stylios CD, Groumpos PP (2003a) An integrated two-level hierarchical decision making system based on fuzzy cognitive maps (FCMs). IEEE Trans Biomed Eng 50(12):1326-1339 10.1109/TBME.2003.819845
26. Papageorgiou EI, Stylios CD, Spyridonos P, Nikiforidis G, Groumpos PP (2003b) Urinary bladder tumor grading using nonlinear Hebbian learning for fuzzy cognitive maps. In: Proceedings of IEE international conference on systems engineering (ICSE 2003), vol 2. pp 542-547
27. Papageorgiou EI, Stylios CD, Groumpos PP (2003c) Fuzzy cognitive map learning based on nonlinear Hebbian rule. In: Gedeon TD, Fung LCC (eds) AI 2003, Lecture notes in artificial intelligence, vol 2903. Springer, Berlin Heidelberg New York, pp 254-266
28. Papageorgiou E, Spyridonos P, Stylios CD, Ravazoula P, Nikiforidis G, Groumpos PP (2004a) The challenge of using soft computing techniques for tumor characterization, In: Seventh international conference on artificial intelligence and soft computing, Zakopane, 7-11 June. Lecture notes in artificial intelligence, vol 3070. Springer, Berlin Heidelberg NewYork, pp 1031-1036
29. Papageorgiou EI, Stylios CD, Groumpos PP (2004b) Active Hebbian learning algorithm to train fuzzy cognitive maps. Int J Approx Reas 35(3):219-249 10.1016/j.ijar.2004.01.001
30. Papageorgiou EI, Parsopoulos KE, Groumpos PP, Vrahatis MN (2004c) Fuzzy cognitive maps learning through swarm intelligence. In: International conference on artificial intelligence and soft computing, Zakopane, 7-11 June. Lecture notes in artificial intelligence, vol 3070. Springer, Berlin Heidelberg New York, pp 344-349
31. Parsopoulos KE, Papageorgiou EI, Groumpos PP, Vrahatis MN (2003) A first study of fuzzy cognitive maps learning using particle swarm optimization. In: Proceedings of IEEE 2003 congress on evolutionary computation. IEEE Press, pp 1440-1447
32. Pelaez CE, Bowles JB (1996) Using fuzzy cognitive maps as a system model for failure modes and effects analysis. Inf Sci 88:177-199 10.1016/ 0020-0255(95)00161-1
33. Stylios CD, Groumpos PP (1999) Fuzzy cognitive maps: A model for intelligent supervisory control systems. Comput Indust 39:229-238 10.1016/S0166-3615(98)00139-0
34. Stylios CD, Groumpos PP (2000) Fuzzy cognitive maps in modeling supervisory control systems. J Intell Fuzzy Syst 8:83-98
35. Stylios CD, Groumpos PP, Georgopoulos VC (1999) An fuzzy cognitive maps approach to process control systems. J Adv Comput Intell 3(5):409-417
36. Sudjianto A, Hassoun M (1995) Statistical basis of nonlinear Hebbian learning and application to clustering. Neural Netw 8(5):707-715 10.1016/ 0893-6080(95)00028-X
37. Xirogiannis G, Stefanou J, Glykas M (2004) A fuzzy cognitive map approach to support urban design. Expert Syst Appl 26(2):257-268 10.1016/ S0957-4174(03)00140-4
38. Xu L (1994) Theories for unsupervised learning: PCA and its nonlinear extensions. In: Proceedings of IEEE international conference on neural networks, vol 2. New York, pp 1252-1257
39. Zadeh LA (1997a) The roles of fuzzy logic and soft computing in the conception, design, and deployment of intelligence systems. In: Nwana HS, Azarmi N (eds) Software agents and soft computing: Towards enhancing machine intelligence concepts and applications. Lecture notes in computer science, vol 1198, pp 83-90
40. Zadeh LA (1997b) What is Soft Computing. Soft Comput 1:1-2