Variation on the number of hidden nodes through multilayer perceptron networks to predict the cycle time
Multilayer Perceptron Network (MLP) has a better prediction Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure of the MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, ther...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2020
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| Online Access: | https://repo.uum.edu.my/id/eprint/28798/1/JICT%2019%2001%202020%2001-19.pdf |
| _version_ | 1825805865679060992 |
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| author | Ahmarofi, Ahmad Afif Ramli, Razamin Zainal Abidin, Norhaslinda Mohd Jamil, Jastini Shaharanee, Izwan Nizal |
| author_facet | Ahmarofi, Ahmad Afif Ramli, Razamin Zainal Abidin, Norhaslinda Mohd Jamil, Jastini Shaharanee, Izwan Nizal |
| author_sort | Ahmarofi, Ahmad Afif |
| collection | UUM |
| description | Multilayer Perceptron Network (MLP) has a better prediction Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure of the MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no rule of thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for producing a new audio product on a production line. The networks were trained and validated through 100 sets of production lots from a selected audio manufacturer. As a result, the 3-2-1 MLP network was the best network based on the lowest square error value compared to the 3-1-1 and 3-3-1 networks. The 3-2-1 predicted the best cycle time of 5 seconds to produce a new audio product. Hence, the prediction result could facilitate production planners in managing assembly processes on the production line. |
| first_indexed | 2024-07-04T06:39:13Z |
| format | Article |
| id | uum-28798 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:39:13Z |
| publishDate | 2020 |
| publisher | Universiti Utara Malaysia Press |
| record_format | eprints |
| spelling | uum-287982022-08-07T03:18:59Z https://repo.uum.edu.my/id/eprint/28798/ Variation on the number of hidden nodes through multilayer perceptron networks to predict the cycle time Ahmarofi, Ahmad Afif Ramli, Razamin Zainal Abidin, Norhaslinda Mohd Jamil, Jastini Shaharanee, Izwan Nizal QA Mathematics Multilayer Perceptron Network (MLP) has a better prediction Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure of the MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no rule of thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for producing a new audio product on a production line. The networks were trained and validated through 100 sets of production lots from a selected audio manufacturer. As a result, the 3-2-1 MLP network was the best network based on the lowest square error value compared to the 3-1-1 and 3-3-1 networks. The 3-2-1 predicted the best cycle time of 5 seconds to produce a new audio product. Hence, the prediction result could facilitate production planners in managing assembly processes on the production line. Universiti Utara Malaysia Press 2020 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/28798/1/JICT%2019%2001%202020%2001-19.pdf Ahmarofi, Ahmad Afif and Ramli, Razamin and Zainal Abidin, Norhaslinda and Mohd Jamil, Jastini and Shaharanee, Izwan Nizal (2020) Variation on the number of hidden nodes through multilayer perceptron networks to predict the cycle time. Journal of Information and Communication Technology, 19 (01). 01-19. ISSN 2180-3862 |
| spellingShingle | QA Mathematics Ahmarofi, Ahmad Afif Ramli, Razamin Zainal Abidin, Norhaslinda Mohd Jamil, Jastini Shaharanee, Izwan Nizal Variation on the number of hidden nodes through multilayer perceptron networks to predict the cycle time |
| title | Variation on the number of hidden nodes through multilayer perceptron networks to predict the cycle time |
| title_full | Variation on the number of hidden nodes through multilayer perceptron networks to predict the cycle time |
| title_fullStr | Variation on the number of hidden nodes through multilayer perceptron networks to predict the cycle time |
| title_full_unstemmed | Variation on the number of hidden nodes through multilayer perceptron networks to predict the cycle time |
| title_short | Variation on the number of hidden nodes through multilayer perceptron networks to predict the cycle time |
| title_sort | variation on the number of hidden nodes through multilayer perceptron networks to predict the cycle time |
| topic | QA Mathematics |
| url | https://repo.uum.edu.my/id/eprint/28798/1/JICT%2019%2001%202020%2001-19.pdf |
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