Variations in the number of hidden nodes through multilayer perceptron networks to predict cycle time
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 no...
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UUM Press
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/27615/ http://umpir.ump.edu.my/id/eprint/27615/ http://umpir.ump.edu.my/id/eprint/27615/7/Variations%20in%20the%20number%20of%20hidden%20nodes.pdf |
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ump-276152020-01-30T01:21:57Z http://umpir.ump.edu.my/id/eprint/27615/ Variations in the number of hidden nodes through multilayer perceptron networks to predict cycle time Ahmad Afif, Ahmarofi Razamin, Ramli Norhaslinda, Zainal Abidin Jastini, Mohd Jamil Izwan Nizal, Shaharanee QA Mathematics T Technology (General) TK Electrical engineering. Electronics Nuclear engineering 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 UUM Press 2020-01 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/27615/7/Variations%20in%20the%20number%20of%20hidden%20nodes.pdf Ahmad Afif, Ahmarofi and Razamin, Ramli and Norhaslinda, Zainal Abidin and Jastini, Mohd Jamil and Izwan Nizal, Shaharanee (2020) Variations in the number of hidden nodes through multilayer perceptron networks to predict cycle time. Journal of ICT, 19 (1). pp. 1-19. ISSN 2180-3862 http://www.jict.uum.edu.my/images/vol19no1jan2020/1-19.pdf |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
| building |
UMP Institutional Repository |
| collection |
Online Access |
| language |
English |
| topic |
QA Mathematics T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| spellingShingle |
QA Mathematics T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Ahmad Afif, Ahmarofi Razamin, Ramli Norhaslinda, Zainal Abidin Jastini, Mohd Jamil Izwan Nizal, Shaharanee Variations in the number of hidden nodes through multilayer perceptron networks to predict cycle time |
| description |
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 |
| format |
Article |
| 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 |
| title |
Variations in the number of hidden nodes through multilayer perceptron networks to predict cycle time |
| title_short |
Variations in the number of hidden nodes through multilayer perceptron networks to predict cycle time |
| title_full |
Variations in the number of hidden nodes through multilayer perceptron networks to predict cycle time |
| title_fullStr |
Variations in the number of hidden nodes through multilayer perceptron networks to predict cycle time |
| title_full_unstemmed |
Variations in the number of hidden nodes through multilayer perceptron networks to predict cycle time |
| title_sort |
variations in the number of hidden nodes through multilayer perceptron networks to predict cycle time |
| publisher |
UUM Press |
| publishDate |
2020 |
| url |
http://umpir.ump.edu.my/id/eprint/27615/ http://umpir.ump.edu.my/id/eprint/27615/ http://umpir.ump.edu.my/id/eprint/27615/7/Variations%20in%20the%20number%20of%20hidden%20nodes.pdf |
| first_indexed |
2023-09-18T22:43:23Z |
| last_indexed |
2023-09-18T22:43:23Z |
| _version_ |
1777417064193982464 |