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 ofthe MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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UUM Press
2019-12-01
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| Series: | Journal of ICT |
| Subjects: | |
| Online Access: | http://e-journal.uum.edu.my/index.php/jict/article/view/jict2020.19.1.1 |
| _version_ | 1818999392991969280 |
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| author | Ahmad Afif Ahmarofi Razamin Ramli Norhaslinda Zainal Abidin Jastini Mohd Jamil Izwan Nizal Shaharanee |
| author_facet | Ahmad Afif Ahmarofi Razamin Ramli Norhaslinda Zainal Abidin Jastini Mohd Jamil Izwan Nizal Shaharanee |
| author_sort | Ahmad Afif Ahmarofi |
| collection | DOAJ |
| 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 ofthe MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no ruleof 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 | 2024-12-20T22:16:42Z |
| format | Article |
| id | doaj.art-6c18ab1df2bc4bbe8d75e292ea758d8b |
| institution | Directory Open Access Journal |
| issn | 1675-414X 2180-3862 |
| language | English |
| last_indexed | 2024-12-20T22:16:42Z |
| publishDate | 2019-12-01 |
| publisher | UUM Press |
| record_format | Article |
| series | Journal of ICT |
| spelling | doaj.art-6c18ab1df2bc4bbe8d75e292ea758d8b2022-12-21T19:25:02ZengUUM PressJournal of ICT1675-414X2180-38622019-12-0119111910.32890/jict2020.19.1.1VARIATION ON THE NUMBER OF HIDDEN NODES THROUGH MULTILAYER PERCEPTRON NETWORKS TO PREDICT THE CYCLE TIMEAhmad Afif Ahmarofi0Razamin Ramli1Norhaslinda Zainal Abidin2Jastini Mohd Jamil3Izwan Nizal Shaharanee4Fakulti Pengurusan Industri, Universiti Malaysia Pahang, MalaysiaSchool of Quantitative Sciences, Universiti Utara Malaysia, MalaysiaSchool of Quantitative Sciences, Universiti Utara Malaysia, MalaysiaSchool of Quantitative Sciences, Universiti Utara Malaysia, MalaysiaSchool of Quantitative Sciences, Universiti Utara Malaysia, MalaysiaMultilayer Perceptron Network (MLP) has a better prediction Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure ofthe MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no ruleof 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.http://e-journal.uum.edu.my/index.php/jict/article/view/jict2020.19.1.1artificial neural networksmultilayer perceptronhidden nodecycle timeproduction line |
| spellingShingle | Ahmad Afif Ahmarofi Razamin Ramli Norhaslinda Zainal Abidin Jastini Mohd Jamil Izwan Nizal Shaharanee VARIATION ON THE NUMBER OF HIDDEN NODES THROUGH MULTILAYER PERCEPTRON NETWORKS TO PREDICT THE CYCLE TIME Journal of ICT artificial neural networks multilayer perceptron hidden node cycle time production line |
| 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 | artificial neural networks multilayer perceptron hidden node cycle time production line |
| url | http://e-journal.uum.edu.my/index.php/jict/article/view/jict2020.19.1.1 |
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