Nonlinear System Identification for a DC Motor using NARMAX Model with Regularization Approach
نویسندگان
چکیده
The approach to the design of direct current (DC) motor varies considerably using advanced methods such as artificial intelligence (AI). However, accuracy issues cannot be totally addressed using conventional methods. This paper presents the study on nonlinear autoregressive moving average with exogenous input (NARMAX) model using multilayer perceptron (MLP) neural network for DC motor modeling. The regularization approach was adopted to improve on the training process of the neural network models. The simulation results show that MLP neural network can fit the NARMAX model and identify the DC motor drive system efficiently only after a few iteration with almost 100% accuracy.
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