Name
Abstract | ||
|---|---|---|
This paper presents a gradient-based iterative identification algorithms for Box-Jenkins systems with finite measurement input/output data. Compared with the pseudo-linear regression stochastic gradient approach, the proposed algorithm updates the parameter estimation using all the available data at each iterative computation (at each iteration), and thus can produce highly accurate parameter estimation. An example is given. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.camwa.2010.06.001 | Computers & Mathematics with Applications |
Keywords | Field | DocType |
box–jenkins models,output data,signal processing,box-jenkins system,iterative algorithms,pseudo-linear regression stochastic gradient,accurate parameter estimation,gradient-based iterative parameter estimation,proposed algorithm,available data,recursive dentification,parameter estimation,gradient-based iterative identification algorithm,finite measurement input,stochastic gradient,iterative computation,linear regression,input output,iterative algorithm | Signal processing,Mathematical optimization,Regression,Iterative method,Computer science,Box–Jenkins,Estimation theory,Computation | Journal |
Volume | Issue | ISSN |
60 | 5 | Computers and Mathematics with Applications |
Citations | PageRank | References |
42 | 1.14 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongqing Wang | 1 | 583 | 23.05 |
| Guowei Yang | 2 | 191 | 16.21 |
| Ruifeng Ding | 3 | 261 | 11.82 |