Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network
Joint Authors
Peng, Jinzhu
Yu, Hongshan
Tang, Yandong
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-10-19
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Hammerstein model has been popularly applied to identify the nonlinear systems.
In this paper, a Hammerstein-type neural network (HTNN) is derived to formulate the well-known Hammerstein model.
The HTNN consists of a nonlinear static gain in cascade with a linear dynamic part.
First, the Lipschitz criterion for order determination is derived.
Second, the backpropagation algorithm for updating the network weights is presented, and the stability analysis is also drawn.
Finally, simulation results show that HTNN identification approach demonstrated identification performances.
American Psychological Association (APA)
Yu, Hongshan& Peng, Jinzhu& Tang, Yandong. 2014. Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1046567
Modern Language Association (MLA)
Yu, Hongshan…[et al.]. Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network. Mathematical Problems in Engineering No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1046567
American Medical Association (AMA)
Yu, Hongshan& Peng, Jinzhu& Tang, Yandong. Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1046567
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1046567