The nature of statistical learning theory
The nature of statistical learning theory
Identifying fuzzy models utilizing genetic programming
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A tutorial on support vector regression
Statistics and Computing
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
T-S fuzzy modeling based on support vector learning
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Complex systems modeling via fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new kernel-based fuzzy clustering approach: support vector clustering with cell growing
IEEE Transactions on Fuzzy Systems
Structure identification of generalized adaptive neuro-fuzzy inference systems
IEEE Transactions on Fuzzy Systems
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
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This paper proposes a novel approach for structure identification of TS fuzzy model using dual kernel-based learning machines. Firstly, a convenient kernel fuzzy C-means clustering algorithm is developed to partition the data set into several clusters. Secondly, a new kernel function which is free of parameter selection is utilized to locate support vectors in each cluster. Finally, the model structure is further simplified by a combination strategy for support vectors. The experimental results show that the resulting model has concise structure and good generalization ability, especially its performance is insensitive to initial clustering number.