Name
Abstract | ||
|---|---|---|
This paper proposes a novel approach for structure identification of TS fuzzy model using dual kernel-based learning machines. Firstly, a convenient kernel fuzzy C-means clustering algorithm is developed to partition the data set into several clusters. Secondly, a new kernel function which is free of parameter selection is utilized to locate support vectors in each cluster. Finally, the model structure is further simplified by a combination strategy for support vectors. The experimental results show that the resulting model has concise structure and good generalization ability, especially its performance is insensitive to initial clustering number. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.neucom.2008.03.002 | Neurocomputing |
Keywords | DocType | Volume |
TS fuzzy modeling,Structure identification,Dual kernel learning machines,Combination strategy | Journal | 71 |
Issue | ISSN | Citations |
16 | 0925-2312 | 1 |
PageRank | References | Authors |
0.36 | 0 | 2 |