3D FcRM modelling in miles per gallon of cars

The new fuzzy c-regression modeling (FcRM) are widely used in order to fit switching regression models. Minimization of objective function yields immediate estimates for different c regression models. The functions of model, estimation technique and results are discussed in this paper. A case st...

Full description

Saved in:
Bibliographic Details
Main Authors: Rusiman, Mohd. Saifullah, Adnan, Robiah, N. Nasibov, Efendi
Format: Book Section
Language:English
Published: Penerbit UTM 2008
Subjects:
Online Access:http://eprints.utm.my/id/eprint/14283/1/RobiahAdnan2008_3DFcRMModellinginMilesPerGallon.pdf
http://eprints.utm.my/id/eprint/14283/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.14283
record_format eprints
spelling my.utm.142832013-04-30T06:04:53Z http://eprints.utm.my/id/eprint/14283/ 3D FcRM modelling in miles per gallon of cars Rusiman, Mohd. Saifullah Adnan, Robiah N. Nasibov, Efendi Q Science (General) The new fuzzy c-regression modeling (FcRM) are widely used in order to fit switching regression models. Minimization of objective function yields immediate estimates for different c regression models. The functions of model, estimation technique and results are discussed in this paper. A case study in miles per gallon (MPG) of different cars using the FcRM modeling was carried out. The 3D graph for significant independent variables for FcRM clustering is shown in this study. The comparison between multiple linear regression and FcRM modeling were done. The mean square error (MSE) was used to find the better model. It was found that the FcRM modeling with lower MSE to be the better model and has great capability in predicting the dependent variable effectively. Penerbit UTM 2008 Book Section PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/14283/1/RobiahAdnan2008_3DFcRMModellinginMilesPerGallon.pdf Rusiman, Mohd. Saifullah and Adnan, Robiah and N. Nasibov, Efendi (2008) 3D FcRM modelling in miles per gallon of cars. In: Advances In Fundamentals And Social Sciences. Penerbit UTM , Johor, pp. 21-30. ISBN 978-983-52-0609-2
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/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Rusiman, Mohd. Saifullah
Adnan, Robiah
N. Nasibov, Efendi
3D FcRM modelling in miles per gallon of cars
description The new fuzzy c-regression modeling (FcRM) are widely used in order to fit switching regression models. Minimization of objective function yields immediate estimates for different c regression models. The functions of model, estimation technique and results are discussed in this paper. A case study in miles per gallon (MPG) of different cars using the FcRM modeling was carried out. The 3D graph for significant independent variables for FcRM clustering is shown in this study. The comparison between multiple linear regression and FcRM modeling were done. The mean square error (MSE) was used to find the better model. It was found that the FcRM modeling with lower MSE to be the better model and has great capability in predicting the dependent variable effectively.
format Book Section
author Rusiman, Mohd. Saifullah
Adnan, Robiah
N. Nasibov, Efendi
author_facet Rusiman, Mohd. Saifullah
Adnan, Robiah
N. Nasibov, Efendi
author_sort Rusiman, Mohd. Saifullah
title 3D FcRM modelling in miles per gallon of cars
title_short 3D FcRM modelling in miles per gallon of cars
title_full 3D FcRM modelling in miles per gallon of cars
title_fullStr 3D FcRM modelling in miles per gallon of cars
title_full_unstemmed 3D FcRM modelling in miles per gallon of cars
title_sort 3d fcrm modelling in miles per gallon of cars
publisher Penerbit UTM
publishDate 2008
url http://eprints.utm.my/id/eprint/14283/1/RobiahAdnan2008_3DFcRMModellinginMilesPerGallon.pdf
http://eprints.utm.my/id/eprint/14283/
_version_ 1643646367252348928
score 13.18916