Name
Abstract | ||
|---|---|---|
A new fuzzy identification approach using support vector regression (SVR) and genetic algorithm (GA) is presented in this paper. Firstly positive definite reference function is utilised to construct a qualified Mercer kernel for SVR. Then an improved GA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy model. Finally, a set of TS fuzzy rules can be extracted from the SVR directly. Simulation results show that the resulting fuzzy model not only costs less fuzzy rules, but also possesses good generalisation ability. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1504/IJMIC.2011.037829 | INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL |
Keywords | Field | DocType |
fuzzy system identification, positive definite reference function, support vector regression, SVR, genetic algorithm | Data mining,Neuro-fuzzy,Defuzzification,Pattern recognition,Fuzzy classification,Fuzzy set operations,Support vector machine,Fuzzy logic,Artificial intelligence,Adaptive neuro fuzzy inference system,Mathematics,Genetic algorithm | Journal |
Volume | Issue | ISSN |
12 | 1-2 | 1746-6172 |
Citations | PageRank | References |
4 | 0.71 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Li | 1 | 28 | 5.33 |
| Yupu Yang | 2 | 332 | 25.20 |
| Zhong Yang | 3 | 5 | 1.06 |
| Changying Zhang | 4 | 4 | 0.71 |