| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 495934 | Applied Soft Computing | 2013 | 15 Pages |
Abstract
⺠This paper proposes a novel self-constructing least-Wilcoxon generalized Radial Basis Function Neural-Fuzzy System (LW-GRBFNFS). ⺠A self-constructing scenario for generating antecedent part with PSO. Least-Wilcoxon (LW) norm is employed for estimating the accuracy of consequent part.⺠⺠The proposed LW-GRBFNFS can provide flexible and dynamic ability for generating more accurate RBFNFS with less sensitivity to noise and outliers.
Related Topics
Physical Sciences and Engineering
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Authors
Ying-Kuei Yang, Tsung-Ying Sun, Chih-Li Huo, Yu-Hsiang Yu, Chan-Cheng Liu, Cheng-Han Tsai,