Proceedings ArticleDOI
A multi-stage least squares method for identifying Hammerstein model nonlinear systems
T. Hsia
- Vol. 15, Iss: 15, pp 934-938
Reads0
Chats0
TLDR
In this article, the parameter identification of nonlinear systems using Hammerstein model and in the presence of correlated output noise is considered and a noniterative four-stage least square solution procedure is proposed.Abstract:
This paper considers the parameter identification of nonlinear systems using Hammerstein model and in the presence of correlated output noise. Existing identification methods are all iterative. The proposed method, called MSLS, is a noniterative four-stage least square solution procedure. Therefore, it is computationally simpler. The estimates so obtained are statistically consistent. Two examples are included to demonstrate the utility of this method.read more
Citations
More filters
Journal ArticleDOI
An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
TL;DR: In this article, an optimal two-stage identification algorithm is presented for Hammerstein-Wiener systems, where two static nonlinear elements surround a linear block, and the algorithm is shown to be convergent in the absence of noise and convergent with probability one in the presence of white noise.
Journal Article
An Optimal Two-stage Identification Algorithm for Hammerstein―Wiener Nonlinear Systems
TL;DR: An optimal two-stage identification algorithm is presented for Hammerstein–Wiener systems where two static nonlinear elements surround a linear block and is shown to be convergent in the absence of noise and convergence with probability one in the presence of white noise.
Journal ArticleDOI
A blind approach to the Hammerstein-Wiener model identification
TL;DR: By using the blind approach, it is shown that all internal variables can be recovered solely based on the output measurements and identification of linear and nonlinear parts can be carried out.
Journal ArticleDOI
A bibliography on nonlinear system identification
TL;DR: The present bibliography represents a comprehensive list of references on nonlinear system identification and its applications in signal processing, communications, and biomedical engineering.
Book ChapterDOI
An optimal two stage identification algorithm for Hammerstein-Wiener nonlinear systems
TL;DR: In this article, an optimal two-stage identification algorithm for Hammerstein-Wiener systems is presented, which is shown to be convergent in the absence of noise and convergent with probability one in the presence of white noise.
References
More filters
Journal ArticleDOI
Nonlinear identification in the presence of correlated noise using a Hammerstein model
TL;DR: In this paper, a simple iterative technique for the estimation of parameters in a Hammerstein model is developed for the case when noise in the output data is correlated, and asymptotically unbiased estimates are obtained rapidly by employing a systematic procedure for the determination of a scalar stepping factor at each iteration.
Journal ArticleDOI
A comparison of two Hammerstein model identification algorithms
TL;DR: Two algorithms for least-squares estimation of parameters of a Hammerstein model are compared and it is demonstrated that the iterative method of Narendra and Gallman produces significantly smaller parameter covariance and slightly smaller rms error than the noniterative method.
Related Papers (5)
An iterative method for the identification of nonlinear systems using a Hammerstein model
Kumpati S. Narendra,P. Gallman +1 more