Identification of DC motor drive system model using Radial Basis Function (RBF) neural network

Identification of DC motor drive system model using Radial Basis Function (RBF) neural network

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 vali...

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Bibliographic Details
Published in:2011 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2011
Main Author: Yassin I.M.; Taib M.N.; Abdul Aziz M.Z.; Abdul Rahim N.; Tahir N.Md.; Johari A.
Format: Conference paper
Language:English
Published: 2011
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855691826&doi=10.1109%2fISIEA.2011.6108685&partnerID=40&md5=9df1bc04a9f5e754de8a179097fa6d93
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Summary: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 x 10 -3 and 8.82 x 10 -3 on the training set and test set, respectively, while fulfilling all validation tests performed. © 2011 IEEE.
ISSN:
DOI:10.1109/ISIEA.2011.6108685

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