Name
Abstract | ||
|---|---|---|
This paper describes a novel nonlinear modeling approach with fuzzy rules and support vector machines. Structure identification is realized by an online clustering method and fuzzy support vector machines, the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. The modeling errors are proven to be robustly stable with bounded uncertainties by a Lyapunov method and an input-to-state stability technique. Comparisons with other related works are made through an application of gas furnace process. The results demonstrate that our approach has good accuracy, and this method is suitable for online fuzzy modeling. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.eswa.2008.09.016 | Expert Syst. Appl. |
Keywords | Field | DocType |
bounded uncertainty,support vector machine,lyapunov method,identification,modeling error,fuzzy support vector machine,online fuzzy modeling,novel nonlinear modeling approach,gas furnace process,fuzzy system,fuzzy system identification svm,parameter learning,fuzzy rule,online clustering method,svm,membership function,model error | Data mining,Neuro-fuzzy,Fuzzy classification,Defuzzification,Computer science,Fuzzy set operations,Fuzzy logic,Artificial intelligence,Fuzzy number,Fuzzy associative matrix,Membership function,Machine learning | Journal |
Volume | Issue | ISSN |
36 | 4 | Expert Systems With Applications |
Citations | PageRank | References |
7 | 0.52 | 17 |
Authors | ||
2 | ||