Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 18, Issue 4, Pages 686-699Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2010.2046904
Keywords
Fuzzy-neural networks (FNNs); fuzzy modeling; support-vector machine (SVM); support-vector regression (SVR); type-2 fuzzy systems
Funding
- National Science Council (NSC), Taiwan [NSC-98-2221-E-005-041-MY2]
- Ministry of Education, Taiwan
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This paper proposes an interval type-2 fuzzy-neural network with support-vector regression (IT2FNN-SVR) for noisy regression problems. The antecedent part in each fuzzy rule of an IT2FNN-SVR uses interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang (TSK) type. The use of interval type-2 fuzzy sets helps improve the network's noise resistance. The network inputs may be numerical values or type-1 fuzzy sets, with the latter being used for further improvements in robustness. IT2FNN-SVR learning consists of both structure learning and parameter learning. The structure-learning algorithm is responsible for online rule generation. The parameters are optimized for structural-risk minimization using a two-phase linear SVR algorithm in order to endow the network with high generalization ability. IT2FNN-SVR performance is verified through comparisons with type-1 and type-2 fuzzy-logic systems and other regression models on noisy regression problems.
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