Enhanced Taguchi's T-method using angle modulated Bat algorithm for prediction
Analysis of multivariate historical information in predicting future state or unknown outcomes is the core function of Taguchi’s T-method. Introduced by Dr. Genichi Taguchi under Mahalanobis-Taguchi system, the T-method combines regression principle and robust quality engineering element in formulat...
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Institute of Advanced Engineering and Science (IAES)
2022
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| 在线阅读: | http://eprints.utm.my/id/eprint/98611/1/FaizirRamlie2022_EnhancedTaguchisTMethodusingAngleModulated.pdf http://eprints.utm.my/id/eprint/98611/ http://dx.doi.org/10.11591/eei.v11i5.4350 |
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my.utm.986112023-01-25T09:34:46Z http://eprints.utm.my/id/eprint/98611/ Enhanced Taguchi's T-method using angle modulated Bat algorithm for prediction Marlan, Zulkifli Marlah Ramlie, Faizir Jamaludin, Khairur Rijal Harudin, Nolia QA Mathematics QA75 Electronic computers. Computer science Analysis of multivariate historical information in predicting future state or unknown outcomes is the core function of Taguchi’s T-method. Introduced by Dr. Genichi Taguchi under Mahalanobis-Taguchi system, the T-method combines regression principle and robust quality engineering element in formulating a predictive model and employs taguchi’s orthogonal array design in optimizing the model through feature or variable selection process. There is a concern regarding the sub-optimality of the T-method prediction accuracy, particularly when the orthogonal array failed to offer a significant number of combinations in search for an optimal subset of features. This is due to the fixed and limited combination offered for evaluation as well as the lack of higher-order interaction of combination. In response to this issue, this paper proposed an angle modulated Bat algorithm to be integrated with the T-method in optimizing the prediction model. A comparison study was conducted using energy efficiency benchmark datasets with the mean absolute error metric used as the performance measure. The results show that the proposed method improved the prediction accuracy by 10.74%, from 6.05 to 5.4, by integrating only four features over the original eight in the prediction model. Institute of Advanced Engineering and Science (IAES) 2022-10 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/98611/1/FaizirRamlie2022_EnhancedTaguchisTMethodusingAngleModulated.pdf Marlan, Zulkifli Marlah and Ramlie, Faizir and Jamaludin, Khairur Rijal and Harudin, Nolia (2022) Enhanced Taguchi's T-method using angle modulated Bat algorithm for prediction. Bulletin of Electrical Engineering and Informatics, 11 (5). pp. 2828-2835. ISSN 2089-3191 http://dx.doi.org/10.11591/eei.v11i5.4350 DOI:10.11591/eei.v11i5.4350 |
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QA Mathematics QA75 Electronic computers. Computer science Marlan, Zulkifli Marlah Ramlie, Faizir Jamaludin, Khairur Rijal Harudin, Nolia Enhanced Taguchi's T-method using angle modulated Bat algorithm for prediction |
| description |
Analysis of multivariate historical information in predicting future state or unknown outcomes is the core function of Taguchi’s T-method. Introduced by Dr. Genichi Taguchi under Mahalanobis-Taguchi system, the T-method combines regression principle and robust quality engineering element in formulating a predictive model and employs taguchi’s orthogonal array design in optimizing the model through feature or variable selection process. There is a concern regarding the sub-optimality of the T-method prediction accuracy, particularly when the orthogonal array failed to offer a significant number of combinations in search for an optimal subset of features. This is due to the fixed and limited combination offered for evaluation as well as the lack of higher-order interaction of combination. In response to this issue, this paper proposed an angle modulated Bat algorithm to be integrated with the T-method in optimizing the prediction model. A comparison study was conducted using energy efficiency benchmark datasets with the mean absolute error metric used as the performance measure. The results show that the proposed method improved the prediction accuracy by 10.74%, from 6.05 to 5.4, by integrating only four features over the original eight in the prediction model. |
| format |
Article |
| author |
Marlan, Zulkifli Marlah Ramlie, Faizir Jamaludin, Khairur Rijal Harudin, Nolia |
| author_facet |
Marlan, Zulkifli Marlah Ramlie, Faizir Jamaludin, Khairur Rijal Harudin, Nolia |
| author_sort |
Marlan, Zulkifli Marlah |
| title |
Enhanced Taguchi's T-method using angle modulated Bat algorithm for prediction |
| title_short |
Enhanced Taguchi's T-method using angle modulated Bat algorithm for prediction |
| title_full |
Enhanced Taguchi's T-method using angle modulated Bat algorithm for prediction |
| title_fullStr |
Enhanced Taguchi's T-method using angle modulated Bat algorithm for prediction |
| title_full_unstemmed |
Enhanced Taguchi's T-method using angle modulated Bat algorithm for prediction |
| title_sort |
enhanced taguchi's t-method using angle modulated bat algorithm for prediction |
| publisher |
Institute of Advanced Engineering and Science (IAES) |
| publishDate |
2022 |
| url |
http://eprints.utm.my/id/eprint/98611/1/FaizirRamlie2022_EnhancedTaguchisTMethodusingAngleModulated.pdf http://eprints.utm.my/id/eprint/98611/ http://dx.doi.org/10.11591/eei.v11i5.4350 |
| _version_ |
1756060036697686016 |
| score |
13.251813 |