Adaptive time delay neural network structures for nonlinear system identification

Abstract In this paper, motivated by the adaptive time delay neural networks (ATDNN), four structures are developed for identifying different classes of nonlinear systems expressed in the input–output representation form. By using certain a priori information about the structure of the nonlinearity of the system one may utilize the appropriate proposed neuro-dynamic structure for identifying the system. The capabilities of the proposed structures for representing the nonlinear systems are shown analytically. Selection criteria for specifying the fixed structural parameters as well as the adaptation laws for updating the adjustable parameters of the identifiers are provided. Simulation results demonstrate that the proposed ATDNN structures are quite effective in identifying a general class of nonlinear systems.

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