Title :
Robust TSK fuzzy modeling for function approximation with outliers
Author :
Chuang, Chen-Chia ; Su, Shun-Feng ; Chen, Song-Shyong
Author_Institution :
Dept. of Electron. Eng., Hwa-Hsia Coll. of Technol. & Commerce, Taipei, Taiwan
fDate :
12/1/2001 12:00:00 AM
Abstract :
The Takagi-Sugeno-Kang (TSK) type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Most approaches for modeling TSK fuzzy rules define their fuzzy subspaces based on the idea of training data being close enough instead of having similar functions. Besides, training data sets algorithms often contain outliers, which seriously affect least-square error minimization clustering and learning algorithms. A robust TSK fuzzy modeling approach is presented. In the approach, a clustering algorithm termed as robust fuzzy regression agglomeration (RFRA) is proposed to define fuzzy subspaces in a fuzzy regression manner with robust capability against outliers. To obtain a more precision model, a robust fine-tuning algorithm is then employed. Various examples are used to verify the effectiveness of the proposed approach. From the simulation results, the proposed robust TSK fuzzy modeling indeed showed superior performance over other approaches
Keywords :
function approximation; fuzzy set theory; modelling; stability; Takagi-Sugeno-Kang fuzzy models; function approximation; fuzzy regression; fuzzy subspaces; neural networks; outliers; pattern recognition; robust TSK fuzzy modeling; Backpropagation algorithms; Clustering algorithms; Function approximation; Fuzzy systems; Least squares approximation; Least squares methods; Minimization methods; Power system modeling; Robustness; Training data;
Journal_Title :
Fuzzy Systems, IEEE Transactions on