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

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.