Abstract
The universal function approximation capabilities of multilayer feedforward neural networks make it a popular choice for modeling dynamic systems. In this paper, identification of dynamic system using time-delay feedforward neural networks with application to DC motor as a case study has been developed. The developed neural network model is a three layer network with nonlinear (sigmoid) activation functions in the hidden layer and linear output layer with input-output delays. Simulation results showed that the neural networks are promising tool for dynamic system identification
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