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Comparison of neural networks prediction and regression analysis (MLR and PCR) in modelling nonlinear system

Different methods for modelling nonlinear system are investigated in this paper. Neural network (NN) techniques, multiple linear regression (MLR) and principal component regression (PCR) are applied to two nonlinear systems which are sine function and distillation column. For the sake of studying th...

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Main Authors: Zainal Ahmad ,, Yong , Fei San
Format: Article
Published: 2007
Online Access:http://journalarticle.ukm.my/2585/
http://www.ukm.my/jkukm/index.php/jkukm
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spelling my-ukm.journal.25852011-10-11T03:45:33Z http://journalarticle.ukm.my/2585/ Comparison of neural networks prediction and regression analysis (MLR and PCR) in modelling nonlinear system Zainal Ahmad , Yong , Fei San Different methods for modelling nonlinear system are investigated in this paper. Neural network (NN) techniques, multiple linear regression (MLR) and principal component regression (PCR) are applied to two nonlinear systems which are sine function and distillation column. For the sake of studying these three distinctive methods, all the data taken is from simulation which is then be seperated into training, testing and validation. Among those different approaches, the NN approach based on the nonlinear prediction technique gives a very good performance in for both case studies. It is also shown that MLR model suffers from glitches due to the collinearity of the input variables whereas PCR model shows good result in the prediction output. As a conclusion, the NN methods exhibit a consistent result with least sum square error (SSE) on the unseen data compared to the other two technique 2007 Article PeerReviewed Zainal Ahmad , and Yong , Fei San (2007) Comparison of neural networks prediction and regression analysis (MLR and PCR) in modelling nonlinear system. Jurnal Kejuruteraan, 19 . pp. 29-42. http://www.ukm.my/jkukm/index.php/jkukm
institution Universiti Kebangsaan Malaysia
building Perpustakaan Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
description Different methods for modelling nonlinear system are investigated in this paper. Neural network (NN) techniques, multiple linear regression (MLR) and principal component regression (PCR) are applied to two nonlinear systems which are sine function and distillation column. For the sake of studying these three distinctive methods, all the data taken is from simulation which is then be seperated into training, testing and validation. Among those different approaches, the NN approach based on the nonlinear prediction technique gives a very good performance in for both case studies. It is also shown that MLR model suffers from glitches due to the collinearity of the input variables whereas PCR model shows good result in the prediction output. As a conclusion, the NN methods exhibit a consistent result with least sum square error (SSE) on the unseen data compared to the other two technique
format Article
author Zainal Ahmad ,
Yong , Fei San
spellingShingle Zainal Ahmad ,
Yong , Fei San
Comparison of neural networks prediction and regression analysis (MLR and PCR) in modelling nonlinear system
author_facet Zainal Ahmad ,
Yong , Fei San
author_sort Zainal Ahmad ,
title Comparison of neural networks prediction and regression analysis (MLR and PCR) in modelling nonlinear system
title_short Comparison of neural networks prediction and regression analysis (MLR and PCR) in modelling nonlinear system
title_full Comparison of neural networks prediction and regression analysis (MLR and PCR) in modelling nonlinear system
title_fullStr Comparison of neural networks prediction and regression analysis (MLR and PCR) in modelling nonlinear system
title_full_unstemmed Comparison of neural networks prediction and regression analysis (MLR and PCR) in modelling nonlinear system
title_sort comparison of neural networks prediction and regression analysis (mlr and pcr) in modelling nonlinear system
publishDate 2007
url http://journalarticle.ukm.my/2585/
http://www.ukm.my/jkukm/index.php/jkukm
_version_ 1643735429541789696
score 13.211869

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