Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network. (19th October 2014)

• Record Type: Journal Article
• Authors: Yu, Hongshan; Peng, Jinzhu; Tang, Yandong
• Other Names: Yan Jun-Juh Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-10-19
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/959507
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.