Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network

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 c...

Full description

Saved in:
Bibliographic Details
Published inMathematical problems in engineering Vol. 2014; no. 2014; pp. 1 - 9
Main Authors Yu, Hongshan, Tang, Yandong, Peng, Jinzhu
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2014
Hindawi Limited
Subjects
Algorithms
Back propagation
Back propagation networks
Computer simulation
Criteria
Dynamical systems
Engineering
Gain
Laboratories
Mathematical models
Neural networks
Nonlinear dynamics
Nonlinear systems
Robotics
Stability analysis
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1024-123X
1563-5147
DOI:10.1155/2014/959507