Particle swarm optimization method in initialization of wavelet neural network model for fed-batch bioprocesses
Wavelet neural network is an alternative to artificial neural network in empirical modeling of industrial processes due to efficient initialization of network parameters that reduces training time. In this paper, particle swarm optimization methods are used for initialization of dilation and transla...
| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/68438/ http://psasir.upm.edu.my/id/eprint/68438/1/Particle%20swarm%20optimization%20method%20in%20initialization%20of%20wavelet%20neural%20network%20model%20for%20fed-batch%20bioprocesses.pdf |
| _version_ | 1848856127480004608 |
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| author | Mamat, Nor Hana Mohd Noor, Samsul Bahari Che Soh, Azura Taip, Farah Saleena Ab Rashid, Ahmad Hazri Jufika Ahmad, Nur Liyana Mohd Yusuff, Ishak |
| author_facet | Mamat, Nor Hana Mohd Noor, Samsul Bahari Che Soh, Azura Taip, Farah Saleena Ab Rashid, Ahmad Hazri Jufika Ahmad, Nur Liyana Mohd Yusuff, Ishak |
| author_sort | Mamat, Nor Hana |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Wavelet neural network is an alternative to artificial neural network in empirical modeling of industrial processes due to efficient initialization of network parameters that reduces training time. In this paper, particle swarm optimization methods are used for initialization of dilation and translational parameters in two wavelet neural network models. Dissolved oxygen models are constructed from real bioprocess data of pilot scale fed-batch bioreactor in polyhydroxyalkanotes (PHA) production and an industrial-scale fed-batch bioreactor in penicillin production. Simulation output of dissolved oxygen and initial mean square error (IMSE) show that the distance and error between initialization and training data are small in PSO method compared to random and heuristic methods. This ensures training phase start very close to target data. |
| first_indexed | 2025-11-15T11:36:43Z |
| format | Conference or Workshop Item |
| id | upm-68438 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:36:43Z |
| publishDate | 2018 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-684382019-06-10T02:18:36Z http://psasir.upm.edu.my/id/eprint/68438/ Particle swarm optimization method in initialization of wavelet neural network model for fed-batch bioprocesses Mamat, Nor Hana Mohd Noor, Samsul Bahari Che Soh, Azura Taip, Farah Saleena Ab Rashid, Ahmad Hazri Jufika Ahmad, Nur Liyana Mohd Yusuff, Ishak Wavelet neural network is an alternative to artificial neural network in empirical modeling of industrial processes due to efficient initialization of network parameters that reduces training time. In this paper, particle swarm optimization methods are used for initialization of dilation and translational parameters in two wavelet neural network models. Dissolved oxygen models are constructed from real bioprocess data of pilot scale fed-batch bioreactor in polyhydroxyalkanotes (PHA) production and an industrial-scale fed-batch bioreactor in penicillin production. Simulation output of dissolved oxygen and initial mean square error (IMSE) show that the distance and error between initialization and training data are small in PSO method compared to random and heuristic methods. This ensures training phase start very close to target data. IEEE 2018 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68438/1/Particle%20swarm%20optimization%20method%20in%20initialization%20of%20wavelet%20neural%20network%20model%20for%20fed-batch%20bioprocesses.pdf Mamat, Nor Hana and Mohd Noor, Samsul Bahari and Che Soh, Azura and Taip, Farah Saleena and Ab Rashid, Ahmad Hazri and Jufika Ahmad, Nur Liyana and Mohd Yusuff, Ishak (2018) Particle swarm optimization method in initialization of wavelet neural network model for fed-batch bioprocesses. In: 2018 8th IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2018), 23-25 Nov. 2018, Penang, Malaysia. (pp. 190-194). 10.1109/ICCSCE.2018.8685024 |
| spellingShingle | Mamat, Nor Hana Mohd Noor, Samsul Bahari Che Soh, Azura Taip, Farah Saleena Ab Rashid, Ahmad Hazri Jufika Ahmad, Nur Liyana Mohd Yusuff, Ishak Particle swarm optimization method in initialization of wavelet neural network model for fed-batch bioprocesses |
| title | Particle swarm optimization method in initialization of wavelet neural network model for fed-batch bioprocesses |
| title_full | Particle swarm optimization method in initialization of wavelet neural network model for fed-batch bioprocesses |
| title_fullStr | Particle swarm optimization method in initialization of wavelet neural network model for fed-batch bioprocesses |
| title_full_unstemmed | Particle swarm optimization method in initialization of wavelet neural network model for fed-batch bioprocesses |
| title_short | Particle swarm optimization method in initialization of wavelet neural network model for fed-batch bioprocesses |
| title_sort | particle swarm optimization method in initialization of wavelet neural network model for fed-batch bioprocesses |
| url | http://psasir.upm.edu.my/id/eprint/68438/ http://psasir.upm.edu.my/id/eprint/68438/ http://psasir.upm.edu.my/id/eprint/68438/1/Particle%20swarm%20optimization%20method%20in%20initialization%20of%20wavelet%20neural%20network%20model%20for%20fed-batch%20bioprocesses.pdf |