Prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c-means method using simulation data
The objective of fuzzy linear regression model (FLRM) to predict the dependent variable and independent variables in vague phenomenon. 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 app...
| Main Authors: | , , , , , |
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| Format: | Article |
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Science Publishing Corporation
2018
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| _version_ | 1825709966183366656 |
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| author | Shafi, Muhammad Ammar Rusiman, Mohd Saifullah Jacob, Kavikumar Amir Hamzah, Nor Shamsidah Che Him, Norziha Mohamad, Nazeera |
| author_facet | Shafi, Muhammad Ammar Rusiman, Mohd Saifullah Jacob, Kavikumar Amir Hamzah, Nor Shamsidah Che Him, Norziha Mohamad, Nazeera |
| author_sort | Shafi, Muhammad Ammar |
| collection | UTHM |
| description | The objective of fuzzy linear regression model (FLRM) to predict the dependent variable and independent variables in vague phenomenon. 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 prediction in a new model and best result in this study. To improve the accuracy of evaluating and predicting, this study employ two measurement error of cross validation statistical technique which are mean square error (MSE) and root mean square error (RMSE). The simulation 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. |
| first_indexed | 2024-03-05T21:50:26Z |
| format | Article |
| id | uthm.eprints-5111 |
| institution | Universiti Tun Hussein Onn Malaysia |
| last_indexed | 2024-03-05T21:50:26Z |
| publishDate | 2018 |
| publisher | Science Publishing Corporation |
| record_format | dspace |
| spelling | uthm.eprints-51112022-01-05T08:39:45Z http://eprints.uthm.edu.my/5111/ Prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c-means method using simulation data Shafi, Muhammad Ammar Rusiman, Mohd Saifullah Jacob, Kavikumar Amir Hamzah, Nor Shamsidah Che Him, Norziha Mohamad, Nazeera QA273-280 Probabilities. Mathematical statistics T55.4-60.8 Industrial engineering. Management engineering The objective of fuzzy linear regression model (FLRM) to predict the dependent variable and independent variables in vague phenomenon. 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 prediction in a new model and best result in this study. To improve the accuracy of evaluating and predicting, this study employ two measurement error of cross validation statistical technique which are mean square error (MSE) and root mean square error (RMSE). The simulation 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. Science Publishing Corporation 2018 Article PeerReviewed 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 (4.3). pp. 419-422. ISSN 2227-524X |
| spellingShingle | QA273-280 Probabilities. Mathematical statistics T55.4-60.8 Industrial engineering. Management engineering Shafi, Muhammad Ammar Rusiman, Mohd Saifullah Jacob, Kavikumar Amir Hamzah, Nor Shamsidah Che Him, Norziha Mohamad, Nazeera Prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c-means method using simulation data |
| title | Prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c-means method using simulation data |
| title_full | Prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c-means method using simulation data |
| title_fullStr | Prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c-means method using simulation data |
| title_full_unstemmed | Prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c-means method using simulation data |
| title_short | Prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c-means method using simulation data |
| title_sort | prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c means method using simulation data |
| topic | QA273-280 Probabilities. Mathematical statistics T55.4-60.8 Industrial engineering. Management engineering |
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