Staff View: Modelling of shre drag tilt velocimeter (DTV) with curvilinear, gompertz and artificial neural network method

Modelling of shre drag tilt velocimeter (DTV) with curvilinear, gompertz and artificial neural network method

Different method of modelling presented in this paper on Shre Drag Tilt Velocimeter non-linear data. The idea of different non-linear modelling method is to know which makes more possible to describe more accurate on interacting effects between velocities and tilt angle when compared among modellers...

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Main Authors:	Muharram, I. A. M., Ismail, Z. H.
Format:	Article
Published:	Universiti Teknikal Malaysia Melaka 2016
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/71669/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011371139&partnerID=40&md5=687cc6f69e1d7081d11c85b73c7dd7d2
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id	my.utm.71669
record_format	eprints
spelling	my.utm.716692017-11-22T12:07:38Z http://eprints.utm.my/id/eprint/71669/ Modelling of shre drag tilt velocimeter (DTV) with curvilinear, gompertz and artificial neural network method Muharram, I. A. M. Ismail, Z. H. TK Electrical engineering. Electronics Nuclear engineering Different method of modelling presented in this paper on Shre Drag Tilt Velocimeter non-linear data. The idea of different non-linear modelling method is to know which makes more possible to describe more accurate on interacting effects between velocities and tilt angle when compared among modellers. The models, which were used are static analytic approximation model, curvilinear bivariate regression model, Gompertz the classical growth model and Artificial Neural Network (ANN) model. Accuracy of the models was determined by mean square error (MSE), mean absolute deviation (MAD), bias and R Square. The datasets gathered from an experiment of Shre DTV at flume were divided into training data and testing data for the purpose of developing and validating all type of models. The difference between the model and the observed value become the forecasting error measurements. For the training data, the lowest MSE, RMSE and better R Square were noted for the Gompertz model. But ANN generalized better on testing data by obtaining lowest MSE, RMSE and higher R Square among others. ANN generalization result is 88.60%, Gompertz is 54.89%, curvilinear is 69.28% and static analytic is -1.29%. Lower bias was also for the neural network test data. As demonstrated by the bias values, only curvilinear model presenting overestimation model while other models produce little or no overestimation of the observed tilt response. Interpretations of the parameters estimation on Gompertz model have been attempted previously. However, focusing on the ability of Shre DTV to predict responses may be more practical than the relevance of parameter estimates. Universiti Teknikal Malaysia Melaka 2016 Article PeerReviewed Muharram, I. A. M. and Ismail, Z. H. (2016) Modelling of shre drag tilt velocimeter (DTV) with curvilinear, gompertz and artificial neural network method. Journal of Telecommunication, Electronic and Computer Engineering, 8 (11). pp. 1-6. ISSN 2180-1843 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011371139&partnerID=40&md5=687cc6f69e1d7081d11c85b73c7dd7d2
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Muharram, I. A. M. Ismail, Z. H. Modelling of shre drag tilt velocimeter (DTV) with curvilinear, gompertz and artificial neural network method
description	Different method of modelling presented in this paper on Shre Drag Tilt Velocimeter non-linear data. The idea of different non-linear modelling method is to know which makes more possible to describe more accurate on interacting effects between velocities and tilt angle when compared among modellers. The models, which were used are static analytic approximation model, curvilinear bivariate regression model, Gompertz the classical growth model and Artificial Neural Network (ANN) model. Accuracy of the models was determined by mean square error (MSE), mean absolute deviation (MAD), bias and R Square. The datasets gathered from an experiment of Shre DTV at flume were divided into training data and testing data for the purpose of developing and validating all type of models. The difference between the model and the observed value become the forecasting error measurements. For the training data, the lowest MSE, RMSE and better R Square were noted for the Gompertz model. But ANN generalized better on testing data by obtaining lowest MSE, RMSE and higher R Square among others. ANN generalization result is 88.60%, Gompertz is 54.89%, curvilinear is 69.28% and static analytic is -1.29%. Lower bias was also for the neural network test data. As demonstrated by the bias values, only curvilinear model presenting overestimation model while other models produce little or no overestimation of the observed tilt response. Interpretations of the parameters estimation on Gompertz model have been attempted previously. However, focusing on the ability of Shre DTV to predict responses may be more practical than the relevance of parameter estimates.
format	Article
author	Muharram, I. A. M. Ismail, Z. H.
author_facet	Muharram, I. A. M. Ismail, Z. H.
author_sort	Muharram, I. A. M.
title	Modelling of shre drag tilt velocimeter (DTV) with curvilinear, gompertz and artificial neural network method
title_short	Modelling of shre drag tilt velocimeter (DTV) with curvilinear, gompertz and artificial neural network method
title_full	Modelling of shre drag tilt velocimeter (DTV) with curvilinear, gompertz and artificial neural network method
title_fullStr	Modelling of shre drag tilt velocimeter (DTV) with curvilinear, gompertz and artificial neural network method
title_full_unstemmed	Modelling of shre drag tilt velocimeter (DTV) with curvilinear, gompertz and artificial neural network method
title_sort	modelling of shre drag tilt velocimeter (dtv) with curvilinear, gompertz and artificial neural network method
publisher	Universiti Teknikal Malaysia Melaka
publishDate	2016
url	http://eprints.utm.my/id/eprint/71669/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011371139&partnerID=40&md5=687cc6f69e1d7081d11c85b73c7dd7d2
_version_	1643656247485923328
score	13.211869

Modelling of shre drag tilt velocimeter (DTV) with curvilinear, gompertz and artificial neural network method

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