A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik-Chervonenkis Dimension
IEEE Transactions on Knowledge and Data Engineering
A New Fuzzy Modeling Approach Based on Support Vector Regression
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Support vector fuzzy adaptive network in regression analysis
Computers & Mathematics with Applications
Support vector interval regression machine for crisp input and output data
Fuzzy Sets and Systems
Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Type-2 Fuzzistics for Symmetric Interval Type-2 Fuzzy Sets: Part 1, Forward Problems
IEEE Transactions on Fuzzy Systems
Discrete Interval Type 2 Fuzzy System Models Using Uncertainty in Learning Parameters
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A bottom-up method for simplifying support vector solutions
IEEE Transactions on Neural Networks
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This paper presents a new interval type-2 fuzzy inference system to handle uncertainty using reduced-set vector learning mechanism based on hybrid kernels. Firstly, a novel concept, interval kernel, is proposed. It establishes a relationship between interval type-2 fuzzy membership and hybrid kernel. According to it, a particular interval type-2 fuzzy inference system is built, which abandons traditional type reduction procedure and utilizes directly defuzzification after inference. Subsequently, the model optimization is realized via a hybrid learning mechanism involving two sub-algorithms: bottom-up simplification algorithm and quadratic programming combined with back propagation algorithm. At last, simulation results show that the interval type-2 fuzzy model obtained possesses nice generalization and transparency.