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...

Full description

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
id 2-s2.0-84855691826
spelling 2-s2.0-84855691826
Yassin I.M.; Taib M.N.; Abdul Aziz M.Z.; Abdul Rahim N.; Tahir N.Md.; Johari A.
Identification of DC motor drive system model using Radial Basis Function (RBF) neural network
2011
2011 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2011


10.1109/ISIEA.2011.6108685
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855691826&doi=10.1109%2fISIEA.2011.6108685&partnerID=40&md5=9df1bc04a9f5e754de8a179097fa6d93
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.


English
Conference paper
author Yassin I.M.; Taib M.N.; Abdul Aziz M.Z.; Abdul Rahim N.; Tahir N.Md.; Johari A.
spellingShingle Yassin I.M.; Taib M.N.; Abdul Aziz M.Z.; Abdul Rahim N.; Tahir N.Md.; Johari A.
Identification of DC motor drive system model using Radial Basis Function (RBF) neural network
author_facet Yassin I.M.; Taib M.N.; Abdul Aziz M.Z.; Abdul Rahim N.; Tahir N.Md.; Johari A.
author_sort Yassin I.M.; Taib M.N.; Abdul Aziz M.Z.; Abdul Rahim N.; Tahir N.Md.; Johari A.
title Identification of DC motor drive system model using Radial Basis Function (RBF) neural network
title_short Identification of DC motor drive system model using Radial Basis Function (RBF) neural network
title_full Identification of DC motor drive system model using Radial Basis Function (RBF) neural network
title_fullStr Identification of DC motor drive system model using Radial Basis Function (RBF) neural network
title_full_unstemmed Identification of DC motor drive system model using Radial Basis Function (RBF) neural network
title_sort Identification of DC motor drive system model using Radial Basis Function (RBF) neural network
publishDate 2011
container_title 2011 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2011
container_volume
container_issue
doi_str_mv 10.1109/ISIEA.2011.6108685
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855691826&doi=10.1109%2fISIEA.2011.6108685&partnerID=40&md5=9df1bc04a9f5e754de8a179097fa6d93
description 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.
publisher
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
_version_ 1809677914228129792

Similar Items