A modified Elman neural network model with application to dynamical systems identification

Abstract

In this paper, an overview of the structure and learning algorithm of the Elman neural network is first presented. A modified Elman network is then proposed by adding new adjustable weights that connect the context nodes with output nodes. Convergence speed of the two network structures are compared. A parallel dynamic system identification scheme based on the modified Elman network is set up as well. Theoretical analysis and simulation results show that our improved neural network-based identification method has the advantage of identifying both linear and nonlinear dynamic systems without any prior knowledge of their orders and structures.