Name
Title | ||
|---|---|---|
Convergence of stochastic gradient estimation algorithm for multivariable ARX-like systems |
Abstract | ||
|---|---|---|
This paper studies the convergence of the stochastic gradient identification algorithm of multi-input multi-output ARX-like systems (i.e., multivariable ARX-like systems) by using the stochastic martingale theory. This ARX-like model contains a characteristic polynomial and differs from the conventional multivariable ARX system. The results indicate that the parameter estimation errors converge to zero under the persistent excitation conditions. The simulation results validate the proposed convergence theorem. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.camwa.2010.01.030 | Computers & Mathematics with Applications |
Keywords | Field | DocType |
multi-input multi-output arx-like system,paper study,stochastic martingale theory,recursive identification,proposed convergence theorem,stochastic gradient identification algorithm,parameter estimation error,conventional multivariable arx system,multivariable systems,arx-like model,stochastic gradient estimation algorithm,parameter estimation,multivariable arx-like system,characteristic polynomial,convergence properties,stochastic gradient | Convergence (routing),Characteristic polynomial,Martingale (probability theory),Mathematical optimization,Multivariable calculus,Control theory,Algorithm,Estimation theory,Mathematics,Gradient estimation | Journal |
Volume | Issue | ISSN |
59 | 8 | Computers and Mathematics with Applications |
Citations | PageRank | References |
72 | 1.77 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanjun Liu | 1 | 552 | 27.52 |
| Jie Sheng | 2 | 127 | 5.36 |
| Ruifeng Ding | 3 | 261 | 11.82 |