Please use this identifier to cite or link to this item:
標題: An Interval Type-2 Fuzzy-Neural Network With Support-Vector Regression for Noisy Regression Problems
作者: Juang, C.F.
Huang, R.B.
Cheng, W.Y.
關鍵字: Fuzzy-neural networks (FNNs);fuzzy modeling;support-vector machine;(SVM);support-vector regression (SVR);type-2 fuzzy systems;logic systems;inference system;prediction;models
Project: Ieee Transactions on Fuzzy Systems
期刊/報告no:: Ieee Transactions on Fuzzy Systems, Volume 18, Issue 4, Page(s) 686-699.
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.
ISSN: 1063-6706
DOI: 10.1109/tfuzz.2010.2046904
Appears in Collections:電機工程學系所

Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.