Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
470822 | Computers & Mathematics with Applications | 2010 | 9 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Dongqing Wang, Guowei Yang, Ruifeng Ding,