|
|
|
| TSK Fuzzy Systems Based on Fuzzy Partition and Support Vector Machines |
| CAI Qian-Feng1,2, HAO Zhi-Feng1,2, LIU Wei2 |
1.College of Computer Science and Engineering, South China University of Technology, Guangzhou 510061
2.Faculty of Applied Mathematics, Guangdong University of Technology, Guangzhou 510090 |
|
|
|
|
Abstract An algorithm is presented to design a Takagi-Sugeno-Kang(TSK) fuzzy system with good generalization ability and robustness in high dimensional feature space by fuzzy clustering algorithms and support vector machines (SVM). Firstly, the antecedent membership functions are obtained by fuzzy clustering algorithms in the product space of the input variables. Then, the corresponding consequent parameters of the TSK model can be estimated from data using SVM. The kernel function of the proposed algorithm can be generated by the antecedent membership functions and it is proved to be a Mercer kernel. Finally, experimental results of three well-known datasets show that the proposed method has better generalization ability and robustness than the traditional techniques of TSK model and SVM.
|
|
Received: 19 June 2008
|
|
|
|
|
|
[1] Sugeno M, Kang G T. Structure Identification of Fuzzy Model. Fuzzy Sets and Systems, 1988, 28(1): 15-33
[2] Takagi T, Sugeno M. Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Trans on System, Man and Cybernetics, 1985, 15(1): 116-132
[3] Jeng J T S, Lee T T. Support Vector Machines for the Fuzzy Neural Networks // Proc of the IEEE International Conference on Systems, Man and Cybernetics. Tokyo, Japan, 1999, Ⅵ: 115-120
[4] Kim K J, Suga Y, Won S. New Fuzzy Inference System Using Support Vector Machine // Proc of the 41st IEEE Conference on Decision and Control. Las Vegas, USA, 2002, Ⅱ: 1349-1354
[5] Lin C T, Yeh C M, Liang S F, et al. Support-Vector-Based Fuzzy Neural Network for Pattern Classification. IEEE Trans on Fuzzy Systems, 2006, 14(1): 31-41
[6] Chen Yixin, Wang J Z. Support Vector Learning for Fuzzy Rule-Based Classification Systems. IEEE Trans on Fuzzy Systems, 2003, 11(6): 716-728
[7] Chen Yixin, Wang J Z. Kernel Machines and Additive Fuzzy Systems: Classification and Function Approximation // Proc of the IEEE International Conference on Fuzzy Systems. St Louis, USA, 2003, Ⅱ: 789-795
[8] Leski J K. Neuro-Fuzzy System with Learning Tolerant to Imprecision. Fuzzy Sets and Systems, 2003, 138(2): 427-439
[9] Leski J K. On Support Vector Regression Machines with Linguistic Interpretation of Kernel Matrix. Fuzzy Sets and Systems, 2006, 157(8): 1092-1113
[10] Celikyilmaz A, Burhan T I. Fuzzy Functions with Support Vector Machines. Information Sciences: An International Journal, 2007, 177(23): 5163-5177
[11] Deng Naiyang, Tian Yingjie. New Method in Data Mining: Support Vector Machine. Beijing, China: Science Press, 2004 (in Chinese)
(邓乃杨,田英杰.数据挖掘的新方法——支持向量机.北京:科学出版社, 2004)
[12] Babuska R. Fuzzy Modeling for Control. Boston, USA: Kluwer Academic, 1998
[13] Yen J, Liang Wang, Charles W G. Improving the Interpretability of TSK Fuzzy Models by Combining Global Learning and Local Learning. IEEE Trans on Fuzzy System, 1998, 6(4): 530-537
[14] Jang J S R. ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans on Systems, Man and Cybernetics, 1993, 23(3): 665-685
[15] Box G E P, Jenkins G M. Time Series Analysis, Forecasting and Control. San Francisco, USA: Holden Day, 1970 |
|
|
|
2009, Vol. 22 
