An improvement of neuro-fuzzy learning algorithm for tuning fuzzy rules

Fuzzy Sets and Systems

Volumn 118, Issue 2, 2001, Pages 339-350

  Shi, Yan ^a   Mizumoto, Masaharu ^b  

^a TOKAI UNIVERSITY   (Japan)

^b OSAKA ELECTRO COMMUNICATION UNIVERSITY   (Japan)

Author keywords

Fuzzy c means clustering algorithm; Fuzzy rule generation; Neuro fuzzy learning algorithm

Indexed keywords

DATA REDUCTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS;

NEURO-FUZZY LEARNING ALGORITHMS;

FUZZY SETS;

EID: 0034154925     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(98)00440-0     Document Type: Article

References (25)

1. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms. 1981;Plenum Press, New York.
2. Cannon R.L., Daveand J.V., Bezdek J.C. Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Trans. Pattern Anal. Machine Intelligence. 8(2):1986;248-255.
3. Cho K.B., Wang B.H. Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction. Fuzzy Sets and Systems. 83(3):1996;325-339.
4. I. Hayashi, H. Nomura, N. Wakami, Acquisition of inference rules by neural network driven fuzzy reasoning, J. Japan Soc. Fuzzy Theory Systems 2 (4) (1990) 585-597 (in Japanese).
5. Horikawa S., Furuhashi Y. Uchikawa T. On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Trans. Neural Networks. 3(5):1992;801-806.
6. H. Ichihashi, Iterative fuzzy modeling and a hierarchical network, Proc. 4th IFSA World Congress, Brussels, vol. Eng., 1991, pp. 49-52.
7. Ichihashi H., Tuksen I.B. A neuro-fuzzy approach to data analysis of pairwise comparisons. Int. J. Approximate Reasoning. 9(3):1993;227-248.
8. H. Ichihashi, T. Watanabe, Learning control by a simplified fuzzy reasoning, J. Japan Soc. Fuzzy Theory Systems 2 (3) (1990) 429-437 (in Japanese).
9. K. Kishida, H. Miyajima, S. Fukumoto, S. Murashima, Construction methods of fuzzy modeling based on learning algorithms, J. Japan Soc. Fuzzy Theory Systems 7 (3) (1995) 585-593 (in Japanese).
10. Kosko B. Neural Networks and Fuzzy Systems. 1992;Prentice-Hall, Englewood Cliffs, NJ.
11. Lee S., Kil R.M. A Gaussian potential function network with hierarchically self-organization learning. IEEE Trans. Neural Networks. 4(1):1991;207-224.
12. M. Maeda, S. Murakami, An automobile tracking control with a fuzzy logic, Proc. 3rd Fuzzy System Symp., Osaka, 1987, pp. 61-66 (in Japanese).
13. T. Masuda, S. Yaku, An acquisition method of fuzzy reasoning rules by neural network with dynamic creative function of hidden units, J. Japan Soc. Fuzzy Theory Systems 5 (2) (1993) 348-358 (in Japanese).
14. H. Nomura, I. Hayashi, N. Wakami, A self-tuning method of fuzzy control by descent method, Proc. 4th IFSA World Congr., Brussels, vol. Eng., 1991, pp. 155-158.
15. H. Nomura, I. Hayashi, N. Wakami, A self-tuning method of fuzzy control by descent method, Proc. IEEE Int. Conf. on Fuzzy Systems, San Diego, 1992, pp. 203-210.
16. Rumelhart D.E., McClelland J.L. the PDP Research Group, Parallel Distributed Processing. 1986;MIT Press, Cambridge, MA.
17. Y. Shi, M. Mizumoto, A new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules, Fuzzy Sets and Systems, in press.
18. Y. Shi, M. Mizumoto, N. Yubazaki, M. Otani, A method of fuzzy rules generation based on neuro-fuzzy learning algorithm, J. Japan Soc. Fuzzy Theory Systems 8 (4) (1996) 695-705 (in Japanese).
19. Y. Shi, M. Mizumoto, N. Yubazaki, M. Otani, A self-tuning method of fuzzy rules based on the gradient descent method, J. Japan Soc. Fuzzy Theory Systems 8 (4) (1996) 757-765 (in Japanese).
20. Y. Shi, M. Mizumoto, N. Yubazaki, M. Otani, A learning algorithm for tuning fuzzy rules based on the gradient descent method, Proc. 5th IEEE Int. Conf. on Fuzzy Systems, New Orleans, vol. 1, 1996, pp. 55-61.
21. K. Tanaka. N. Kashiwagi, H. Nakajima, Identification of a pulse rate prediction model for activity sensing pacer via a fuzzy modeling method, J. Japan Soc. Fuzzy Theory Systems 6 (4) (1994) 756-764 (in Japanese).
22. Wang L.X. Adaptive Fuzzy Systems and Control. 1994;Prentice-Hall, Englewood Cliffs, NJ.
23. L.X. Wang, J.M. Mendel, Back-propagation fuzzy system as nonlinear dynamic system identifiers, Proc. IEEE Int. Conf. on Fuzzy Systems, San Diego, 1992, pp. 1409-1416.
24. Yager R.R., Filev D.P. Essentials of Fuzzy Modeling and Control. 1994;Wiley, New York.
25. Yager R.R., Filev D.P. Generation of fuzzy rules by mountain clustering. J. Intelligent Fuzzy Systems. 2(3):1994;209-219.