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Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network

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  • Hongshan Yu
  • Jinzhu Peng
  • Yandong Tang

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.

Suggested Citation

  • Hongshan Yu & Jinzhu Peng & Yandong Tang, 2014. "Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, October.
    • Handle: RePEc:hin:jnlmpe:959507
    DOI: 10.1155/2014/959507
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