Name
Abstract | ||
|---|---|---|
Identification of a T-S fuzzy ARMAX model is addressed in this paper. From the fuzzy ARMAX model, a fuzzy one-step ahead prediction model is developed. A recursive least square algorithm is then proposed to identify the parameters in the consequent part of a T-S fuzzy ARMAX system. Properties of the parameter estimates are rigorously derived. This work is an extension of the results of identification of stochastic linear systems. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/FUZZY.2004.1375548 | FUZZ-IEEE |
Keywords | Field | DocType |
fuzzy system,fuzzy set theory,fuzzy one step ahead prediction model,stochastic systems,recursive least square algorithm,parameter estimation,autoregressive moving average processes,t-s fuzzy armax model,least squares approximations,fuzzy systems,linear systems,recursive estimation,parameter identification,stochastic linear systems,predictive models,linear system,stochastic processes,fuzzy sets,stochastic resonance,polynomials,prediction model | Mathematical optimization,Linear system,Computer science,Fuzzy logic,Fuzzy set,Least mean square algorithm,Artificial intelligence,Fuzzy control system,Machine learning,Recursion | Conference |
Volume | ISSN | ISBN |
2 | 1098-7584 | 0-7803-8353-2 |
Citations | PageRank | References |
1 | 0.36 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bore-Kuen Lee | 1 | 87 | 11.30 |
| Bor-Sen Chen | 2 | 2640 | 228.84 |