Adaptive network-based fuzzy inference system with pruning

Proceedings of the SICE Annual Conference

Volumn 1, Issue , 2003, Pages 140-143

  Kim, Chang Hyun ^a   Lee, Ju Jang ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

ANFIS; Fuzzy model; Impact factor; Pruning

Indexed keywords

FUZZY LOGIC; FUZZY NEURAL NETWORKS; FUZZY RULES; MEMBERSHIP FUNCTIONS;

ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM; ANFIS; APPROXIMATION PROPERTIES; FUZZY MODELING; IMPACT FACTOR; NONLINEAR FUNCTION APPROXIMATION; PRUNING; TAKAGI SUGENO FUZZY MODELS;

FUZZY INFERENCE;

EID: 84901590181     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. T. Takagi and M. Sugeno, Fuzzy identification of systems and its application to modeling and control, IEEE Trans. Syst., Man, Cybern., Vol. 15, pp. 116-132, 1985.
2. E. H. Mamdani and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man-Machine Studies, Vol. 7, no. 1, pp. 1-12, 1975.
3. R. M. Tong, The construction and evaluation of fuzzy models, Advances in Fuzzy Set Theory and Applications, M. M. Gupta, R. K. Ragade, and R. R. Yager, Eds. Amsterdam, The Netherlands: North-Holland, pp. 559-576, 1979.
4. L. X. Wang and J. M. Mendel, Generating fuzzy rules by learning from examples, IEEE Trans. Syst., Man, Cybern., Vol. 22, pp. 1414-1427, 1992.
5. H. Nomura, I. Hayashi, and N. Wakami, A learning method of fuzzy inference rules by descent method, Proc. IEEE Int. Conf. Fuzzy Systems, pp. 203-210, 1992.
6. S. Horikawa, T. Furuhashi, and Y. Uchikawa, On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm, IEEE Trans. Neural Networks, Vol. 3, pp. 801-806, 1992.
7. J. S. R. Jang, ANFIS: Adaptive-network-based fuzzy inference systems, IEEE Trans. Syst., Man, Cybern., Vol. 23, pp. 665-685, 1993.
8. M. Sugeno and T. Yasukawa, A fuzzy-logic based approach to qualitative modeling, IEEE Trans. Fuzzy Syst., Vol. 1, Feb. 1993.
9. W. Pedrycz, Conditional fuzzy c-means, Patt. Recog., Vol. 17, pp. 625-631, 1996.
10. M. L. Hadjili and V. Wertz, Takagi-Sugeno Fuzzy Modeling Incorporating Input Variable Selection, IEEE Trans. Fuzzy Syst., Vol. 10, no. 6, pp. 728-742, 2002.
11. L. Wang and J. Yen, Extracting fuzzy rules for system modelling using a hybrid of genertic algorithms and Kalman filter, Fuzzy Sets and Syst., Vol. 101, pp 353-362, 1999.
12. J. Yen and L. Wang, Simplifying fuzzy rule-base models using orthogonal transforamtion method, IEEE Trans. Syst, Man. Cyb., Vol 29, No. 1, pp 13-24, 1999.
13. K. Gasso, G. Mourot and J. Ragot, Fuzzy rule base optimisation: a pruning and merging approach, Proc. IEEE Int. Conf. Syst., Man, Cybern., Vol. 1, pp. 67-72, 2000.
14. J. Yen and L. Wang, Application of statistical information criteria for optimal fuzzy construction, IEEE Trans. Fuzzy Syst., Vol. 6, pp. 362-371, 1998.
15. M. Setnes and H. Roubos, GA-fuzzy modeling and classification: complexity and performance, IEEE Trans. Fuzzy Syst., Vol. 8, pp. 509-522, 2000.