Specificity shift in solving fuzzy relational equations

Fuzzy Sets and Systems

Volumn 106, Issue 2, 1999, Pages 211-220

  Hirota, Kaoru ^a   Pedrycz, Witold ^b  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^b UNIVERSITY OF MANITOBA   (Canada)

Author keywords

Approximate solutions; Fuzzy relational equations; Gradient oriented methods; Hybrid methods; Max t composition; Specificity shift

Indexed keywords

EID: 0040958822     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(97)00286-8     Document Type: Article

References (21)

1. [1] A. Blanco, M. Delgado, I. Requena, Smoothed max-min methods for identifying fuzzy systems, J. Fuzzy Math. 3 (1995) 723-741.
2. [2] A. Di Nola, S. Sessa, On the set of composite fuzzy relation equations, Fuzzy Sets and Systems 9 (1983) 275-285.
3. [3] A. Di Nola, S. Sessa, W. Pedrycz, E. Sanchez, Fuzzy Relation Equations and Their Applications in Knowledge Engineering, Kluwer Academic Press, Dordrecht, 1989.
4. [4] M.B. Ghalia, A.T. Alouani, Prediction of complex systems' behavior described by fuzzy relational equations, Proc. 3rd IEEE Internat. Conf. on Fuzzy Systems, Orlando, FL, 26 June-29 June 1994, vol. II, pp. 1040-1043.
5. [5] S. Gottwald, On the existence of solutions of systems of fuzzy equations, Fuzzy Sets and Systems 12 (1984) 301-302.
6. [6] S. Gottwald, W. Pedrycz, Solvability of fuzzy relational equations and manipulation of fuzzy data, Fuzzy Sets and Systems 18 (1986) 1-21.
7. [7] S. Gottwald, Fuzzy Sets and Fuzzy Logic, Vieweg Verlag, Wiesbaden, 1993.
8. [8] K. Hirota, W. Pedrycz, Fuzzy system identification via probabilistic sets, Inform. Sci. 28 (1982) 553-577.
9. [9] K. Hirota, W. Pedrycz, Solving fuzzy relational equations through logical filtering, Fuzzy Sets and Systems, to appear.
10. [10] N. Ikoma, W. Pedrycz, K. Hirota, Estimation of fuzzy relational matrix by using probabilistic descent method, Fuzzy Sets and Systems 57 (1993) 335-349.
11. [11] W. Pedrycz, Numerical and applicational aspects of fuzzy relational equations, Fuzzy Sets and Systems 11 (1983) 1-18.
12. [12] W. Pedrycz, Applications of fuzzy relational equations for methods of reasoning in presence of fuzzy data, Fuzzy Sets and Systems 16 (1985) 163-175.
13. [13] W. Pedrycz, Neurocomputations in relational systems, IEEE Trans. Pattern Anal. Mach. Intelligence 13 (1991) 289-296.
14. [14] W. Pedrycz, Fuzzy Sets Engineering, CRC Press, Boca Raton, 1995.
15. [15] W. Pedrycz, Genetic algorithms for learning in fuzzy relational structures, Fuzzy Sets and Systems 69 (1995) 37-52.
16. [16] W. Pedrycz, A.F. Rocha, Fuzzy-set based models of neurons and knowledge-based networks, IEEE Trans. Fuzzy Systems 1 (1993) 254-266.
17. [17] C.A. Reyes-Garcia, W. Bandler, The use of trapezoidal function in a linguistic fuzzy relational neural network for speech recognition, Proc. IEEE Internat. Conf. on Neural Networks, 28 June-2 July 1994, Orlando, FL, vol. VII, pp. 4487-4492.
18. [18] T. Saito, M. Mukaidono, A learning algorithm for max-min network and its application to solve fuzzy relation equations, Proc. 2nd Internat. Conf. on Fuzzy Logic and Neural Networks, Iizuka '92, pp. 184-187.
19. [19] R.R. Yager, Entropy and specificity in a mathematical theory of evidence, Int. J. General Systems 9 (1983) 249-260.
20. [20] X. Zhang, C. Hang, S. Tan, P.Z. Wang, The delta rule and learning for min-max neural networks, Proc. IEEE Internat. Conf. on Neural Networks, 28 June-2 July 1994, Orlando, FL, vol. I, pp. 38-43.
21. [21] J. Valente de Oliveira, Neuron inspired learning rules for fuzzy relational structures, Fuzzy Sets and Systems 57 (1993) 41-53.