An Interval Type-2 Fuzzy-Neural Network With Support-Vector Regression for Noisy Regression Problems
| Autor: | Chia-Feng Juang, Ren-Bo Huang, Wei-Yuan Cheng |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Fuzzy rule
Artificial neural network business.industry Applied Mathematics Fuzzy set Pattern recognition Regression analysis Fuzzy control system computer.software_genre Fuzzy logic Support vector machine Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering Structural risk minimization Artificial intelligence Data mining business computer Mathematics |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 18:686-699 |
| ISSN: | 1941-0034 1063-6706 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|