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Prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c-means method using simulation data

Shafi, Muhammad Ammar and Rusiman, Mohd Saifullah and Jacob, Kavikumar and Amir Hamzah, Nor Shamsidah and Che Him, Norziha and Mohamad, Nazeera (2018) Prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c-means method using simulation data. International Journal of Engineering & Technology, 7 (4.30). p. 419. ISSN 2227524X

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Abstract

The objective of fuzzy linear regression model (FLRM) to predict the dependent variable and independent variables in vague phenome-non. In this study, several models such as fuzzy linear regression model (FLRM), fuzzy linear regression with symmetric parameter (FLWSP) and a hybrid model have been applied to be evaluated by 1000 rows in 1 simulation data. Moreover, the hybrid method was applied between fuzzy linear regression with symmetric parameter (FLRWSP) and fuzzy c-mean (FCM) method to get the effective pre-diction in a new model and best result in this study. To improve the accuracy of evaluating and predicting, this study employ two meas-urement error of cross validation statistical technique which are mean square error (MSE) and root mean square error (RMSE). The simu-lation result suggests that comparison among models using two measurement errors should be to determine the best results. Finally, this study notes that the new hybrid model of FLRWSP and FCM is verified to be a good model with the least value of MSE and RMSE measurement errors.

Item Type: Article
Uncontrolled Keywords: Fuzzy C-means; Fuzzy linear regression; Hybrid Model
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Applied Science and Technology > Department of Mathematics and Statistic
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 31 Oct 2019 02:33
Last Modified: 31 Oct 2019 02:33
URI: http://eprints.uthm.edu.my/id/eprint/11787
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