An interval type-2 fuzzy-neural network with support-vector regression for noisy regression problems

  • Authors:

  • Chia-Feng Juang;Ren-Bo Huang;Wei-Yuan Cheng

  • Affiliations:

  • Department of Electrical Engineering, National Chung-Hsing University, Taichung, Taiwan;Department of Electrical Engineering, National Chung-Hsing University, Taichung, Taiwan;Department of Electrical Engineering, National Chung-Hsing University, Taichung, Taiwan

  • Venue:
  • IEEE Transactions on Fuzzy Systems
  • Year:
  • 2010

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Abstract

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.