Title :
Identification of DC motor drive system model using Radial Basis Function (RBF) Neural Network
Author :
Yassin, Ihsan Mohd ; Taib, Mohd Nasir ; Aziz, Mohd Zafran Abdul ; Rahim, Norasmadi Abdul ; Tahir, Nooritawati Md ; Johari, Aiman
Author_Institution :
Fac. of Electr. Eng., Univ. Teknol. Mara, Shah Alam, Malaysia
Abstract :
In this paper, we present a Radial Basis Function Neural Network (RBFNN)-based Nonlinear Auto-Regressive Model with Exegeneous Inputs (NARX) model of a DC motor drive controller model by (Rahim, 2004). Tests were conducted to measure the accuracy of the model (using One Step Ahead (OSA) and its validity (using correlation tests and histogram analysis). The resulting model produced Mean Square Error (MSE) of 8.53 × 10-3 and 8.82 × 10-3 on the training set and test set, respectively, while fulfilling all validation tests performed.
Keywords :
DC motor drives; machine control; mean square error methods; neurocontrollers; radial basis function networks; DC motor drive controller model; DC motor drive system model; MSE; NARX model; OSA test; RBF neural network; correlation tests; histogram analysis; mean square error; nonlinear autoregressive model-with-exegeneous inputs; one-step ahead test; radial basis function neural network; test set; training set; validation test; Correlation; DC motors; Mathematical model; System identification; Testing; Torque; Training; NARX; radial basis function neural network; system identification;
Conference_Titel :
Industrial Electronics and Applications (ISIEA), 2011 IEEE Symposium on
Conference_Location :
Langkawi
Print_ISBN :
978-1-4577-1418-4
DOI :
10.1109/ISIEA.2011.6108685
