The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM)
The optimization of the manufacture of cotton yarns involves several processes, while the prediction of yarn quality parameters forms an important area of investigation. This research work concentrated on the prediction of cotton yarn elongation. Cotton lint and yarn samples were collected in textil...
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| Format: | Article |
| Language: | English |
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University of Ljubljana Press (Založba Univerze v Ljubljani)
2017-03-01
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| Series: | Tekstilec |
| Subjects: | |
| Online Access: | http://www.tekstilec.si/wp-content/uploads/2017/03/10.14502Tekstilec2017.60.65-72.pdf |
| _version_ | 1797934592058982400 |
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| author | Josphat Igadwa Mwasiagi |
| author_facet | Josphat Igadwa Mwasiagi |
| author_sort | Josphat Igadwa Mwasiagi |
| collection | DOAJ |
| description | The optimization of the manufacture of cotton yarns involves several processes, while the prediction of yarn quality parameters forms an important area of investigation. This research work concentrated on the prediction of cotton yarn elongation. Cotton lint and yarn samples were collected in textile factories in Kenya.The collected samples were tested under standard testing conditions. Cotton lint parameters, machine parameters and yarn elongation were used to design yarn elongation prediction models. The elongation prediction models used three network training algorithms, including backpropagation (BP), an extreme learning machine (ELM), and a hybrid of differential evolution (DE) and an ELM referred to as DE-ELM. The prediction models recorded a mean squared error (mse) value of 0.001 using 11, 43 and 2 neurons in the hidden layer for the BP, ELM and DE-ELM models respectively. The ELM models exhibited faster training speeds than the BP algorithms, but required more neurons in the hidden layer than other models. The DEELM hybrid algorithm was faster than the BP algorithm, but slower than the ELM algorithm. |
| first_indexed | 2024-04-10T18:01:25Z |
| format | Article |
| id | doaj.art-334345c2b6a64eb4b93ece7befe12382 |
| institution | Directory Open Access Journal |
| issn | 0351-3386 2350-3696 |
| language | English |
| last_indexed | 2024-04-10T18:01:25Z |
| publishDate | 2017-03-01 |
| publisher | University of Ljubljana Press (Založba Univerze v Ljubljani) |
| record_format | Article |
| series | Tekstilec |
| spelling | doaj.art-334345c2b6a64eb4b93ece7befe123822023-02-02T15:18:45ZengUniversity of Ljubljana Press (Založba Univerze v Ljubljani)Tekstilec0351-33862350-36962017-03-01601657210.14502/Tekstilec2017.60.65-72The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM)Josphat Igadwa Mwasiagi0School of Engineering, Moi University, PO Box 3900 (30100), Eldoret, KenyaThe optimization of the manufacture of cotton yarns involves several processes, while the prediction of yarn quality parameters forms an important area of investigation. This research work concentrated on the prediction of cotton yarn elongation. Cotton lint and yarn samples were collected in textile factories in Kenya.The collected samples were tested under standard testing conditions. Cotton lint parameters, machine parameters and yarn elongation were used to design yarn elongation prediction models. The elongation prediction models used three network training algorithms, including backpropagation (BP), an extreme learning machine (ELM), and a hybrid of differential evolution (DE) and an ELM referred to as DE-ELM. The prediction models recorded a mean squared error (mse) value of 0.001 using 11, 43 and 2 neurons in the hidden layer for the BP, ELM and DE-ELM models respectively. The ELM models exhibited faster training speeds than the BP algorithms, but required more neurons in the hidden layer than other models. The DEELM hybrid algorithm was faster than the BP algorithm, but slower than the ELM algorithm.http://www.tekstilec.si/wp-content/uploads/2017/03/10.14502Tekstilec2017.60.65-72.pdfcotton yarnelongationbackpropagationextreme learning machinesprediction |
| spellingShingle | Josphat Igadwa Mwasiagi The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM) Tekstilec cotton yarn elongation backpropagation extreme learning machines prediction |
| title | The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM) |
| title_full | The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM) |
| title_fullStr | The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM) |
| title_full_unstemmed | The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM) |
| title_short | The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM) |
| title_sort | prediction of yarn elongation of kenyan ring spun yarn using extreme learning machines elm |
| topic | cotton yarn elongation backpropagation extreme learning machines prediction |
| url | http://www.tekstilec.si/wp-content/uploads/2017/03/10.14502Tekstilec2017.60.65-72.pdf |
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