Hierarchical takagi-sugeno type fuzzy system for diabetes mellitus forecasting

Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC

Volumn 3, Issue , 2008, Pages 1265-1270

  Sharifi, Arash ^a   Vosolipour, Asiyeh ^b   Sh, Mahdi Aliyari ^b   Teshnehlab, Mohammad ^b  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b K N TOOSI UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

ANFIS structure; Backpropagation algorithm; Diabetic mellitus; Hierarchical Takagi Sugeno fuzzy system

Indexed keywords

BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; CONTROL THEORY; CYBERNETICS; DATA HANDLING; FEEDBACK CONTROL; FEEDFORWARD NEURAL NETWORKS; FUZZY INFERENCE; FUZZY SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; ROBOT LEARNING; STATISTICAL TESTS; SUGAR (SUCROSE);

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS; ANFIS STRUCTURE; BLOOD TESTS; DATA SETS; DIABETES MELLITUS; DIABETIC MELLITUS; GROUP METHOD OF DATA HANDLINGS; HIERARCHICAL TAKAGI-SUGENO FUZZY SYSTEM; MULTI LAYERS; NETWORK STRUCTURES; NEURO FUZZY; PARTIAL DESCRIPTIONS; RADIAL BASIS NETWORKS; SUGENO TYPES; TRAINING AND TESTING;

FUZZY LOGIC;

EID: 57749104225     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2008.4620599     Document Type: Conference Paper

References (22)

1. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its application to modeling and control," IEEE Trans. Syst. Man, Cybern., vol. SMC-15, no. 1, pp. 116-132, Jan. 1985.
2. C. Xu and Y. Liu, "Fuzzy model identification and self learning for dynamic systems," IEEE Trans. Syst. Man, Cybern., vol. SMC-17, pp. 683-689, Jul./Aug. 1987.
3. H. Ying, "Theory and application of a novel fuzzy PID controller using a simplified Takagi-Sugeno rule scheme," Inf. Sci., vol. 123, no. 3-4, pp. 281-293, 2000.
4. Q. Gan and C. J. Harris, "Fuzzy local linearization and logic basis function expansion in nonlinear system modeling," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 29, no. 4, pp. 559-565, Aug. 1999.
5. Y. Shi, R. Eberhart, and Y. chen, "Implementation of evolutionary fuzzy systems," IEEE Trans. Fuzzy Syst., vol. 7, no. 2, pp. 109-119, Apr. 1999.
6. C. -K. Lin and S.-D. Wang, "Fuzzy system identification using an adaptive learning rule with terminal attractors," J. Fuzzy Sets Syst., pp. 343-352, 1999.
7. S. -J. Kang, C. -H. Woo, H. -S. Hwang, and K. B. Woo, "Evolutionary design of fuzzy rule base for nonlinear system modeling and control," IEEE Trans. Fuzzy Syst., vol. 8, no. 1, pp. 37-45, Feb. 2000.
8. Y. -P. Huang and S. -F. Wang, "Designing a fuzzy model by adaptive macroevolution genetic algorithms," Fuzzy Sets Syst., vol. 113, pp. 367-379, 2000.
9. T. P.Wu and S. M. Chen, "A new method for constructing membership functions and fuzzy rules from training examples," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 29, no. 1, pp. 25-40, Feb. 1999.
10. T. Ohtani, H. Ichihashi, K. Nagasaka and T. Miyoshi: Successive Projection Method for Fast Learning Algorithm of Neurofuzzy GMDH; Proc. of 4th Int. Conf. on Soft Computing, Vol. 1, pp. 432-435, 1996.
11. T. Ohtani, H. Ichihashi, K. Nagasaka and T. Miyoshi: Function Approximation by Neurofuzzy GMDH with Error Backpropagation Learning; J. of Japan Industrial Management Association, Vol. 47, No. 6, pp. 384-392, 1997.
12. Jyh-Shing Roger Jang, "ANFIS: Adaptive Network-Based Fuzzy Inference System", IEEE Trans. Sys.,Man and Cybernetics., Vol. 23, No.3, May/June 1993.
13. A. G. Ivakhneko, Polynomial Theory of Complex Systems. IEEE Trans. Sys. Man and Cybem., Vol. SMC-1, No. 4, pp. 364-378, 1971.
14. J. Moody and C. J. Darken: Fast Learning in Networks of Locally-Tuned Processing Unit, Neural Computation, Vol. 1, No. 2, pp. 281-294, 1989.
15. J. Moody and C. J. Darken: Learning with Localized Receptive Fields, Proc. of the 1988 Connectionist Models Summer School, 1988.
16. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its application to modeling and control," IEEE Trans. Syst. Man, Cybern., vol. SMC-15, no. 1, pp. 116-132, Jan. 1985.
17. R. Babuska, "Fuzzy modeling and identification," Ph.D. dissertation, Univ. Delft, Delft, The Netherlands, 1996.
18. P. Angelov and D. Filev, "An approach to online identification of Takagi-Sugeno fuzzy models," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 1, pp. 484-498, Feb. 2004.
19. N. Kasabov and Q. Song, "DENFIS: Dynamic, evolving neural-fuzzy inference system and its application for time-series prediction," IEEE Trans. Fuzzy Syst., vol. 10, no. 1, pp. 144-154, Feb. 2002.
20. 13. Yager, R. R. and Zadeh, L. A., "Fuzzy Sets Neural Networks, and Soft Computing", Van Nostrand Reinhold, 1994.
21. Manish Kumar, Devendra P. Garg "Intelligent Learning of Fuzzy Logic Controllers via Neural Network and Genetic Algorithm", Proceedings of2004 JUSFA 2004 Japan - USA Symposium on Flexible Automation Denver, Colorado, July 19-2 1, 2004.
22. L. Prechelt, "Probenl- A set of neural network benchmark problems and benchmarking rules.," Fakultat fur Informatik, Universitat Karlsruhe, Karlsruhe, Germany, Technical 21/94, September 1994.