Name
Abstract | ||
|---|---|---|
In this paper, we present an improved learning scheme for extracting T-S fuzzy rules from data samples, whereby a neuro-fuzzy architecture implements the T-S fuzzy system using ellipsoidal basis functions. The salient characteristics of this approach are as follows: 1) A novel structure learning algorithm incorporating a pruning strategy into new growth criteria is developed. 2) Compact fuzzy rules can be extracted from training data. 3) The linear least squares (LLS) method is employed to update consequent parameters, and thereby contributing to high approximation accuracy. Simulation studies and comprehensive comparisons with other well-known algorithms demonstrate the effective and superior performance of our proposed scheme in terms of compact structure and promising accuracy. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39068-5_7 | ISNN (2) |
Keywords | DocType | Citations |
compact structure,promising accuracy,novel structure,t-s fuzzy rule,data sample,improved learning scheme,proposed scheme,high approximation accuracy,t-s fuzzy system,compact fuzzy rule | Conference | 1 |
PageRank | References | Authors |
0.37 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Wang | 1 | 333 | 18.88 |
| Xuming Wang | 2 | 1 | 0.37 |
| Yue Tan | 3 | 1 | 0.71 |
| Pingbo Shao | 4 | 1 | 0.37 |
| Min Han | 5 | 761 | 68.01 |