Neuro-fuzzy modeling of a conveyor-belt grain dryer
The grain drying process is one of the most critical post-harvest operations in modern agricultural production. Development of a reliable control strategy for this process plays an important role in improving the overall efficiency and productivity of the drying process. In control system design, th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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WFL Publisher
2010
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| Online Access: | http://psasir.upm.edu.my/id/eprint/15793/1/Neuro.pdf |
| _version_ | 1825945690193264640 |
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| author | Lutfy, Omar F. Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Abbas, Kassim Ali Mansor, Hasmah |
| author_facet | Lutfy, Omar F. Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Abbas, Kassim Ali Mansor, Hasmah |
| author_sort | Lutfy, Omar F. |
| collection | UPM |
| description | The grain drying process is one of the most critical post-harvest operations in modern agricultural production. Development of a reliable control strategy for this process plays an important role in improving the overall efficiency and productivity of the drying process. In control system design, the first problem to be addressed is the availability of a relatively simple and accurate model of the process to be controlled. However, the majority of the models developed for the grain drying process and the numerical methods required to solve them are characterized by their highly complex nature, and thus they are not suitable to be utilized in control system design. This paper presents an application of a neuro-fuzzy system, in particular the adaptive neuro-fuzzy inference system (ANFIS), to develop a data-driven model for a conveyor-belt grain dryer. This model can be easily used in control system design to develop a reliable control strategy for the drying process. By conducting a real-time experiment to dry paddy grains, a set of input-output data were collected from a laboratory-scale conveyor-belt grain dryer. These data were then presented to the ANFIS network in order to learn the nonlinear functional relationship between the input and output data by this network. Based on utilizing a clustering method to identify the structure of the ANFIS network, the resulting ANFIS model has shown a remarkable modeling performance to represent the drying process. In addition, the modeling result achieved by this ANFIS model was compared with those of an autoregressive with exogenous input (ARX) model and an artificial neural network (ANN) model, and the results clearly showed the superiority of the ANFIS model. |
| first_indexed | 2024-03-06T07:35:24Z |
| format | Article |
| id | upm.eprints-15793 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T07:35:24Z |
| publishDate | 2010 |
| publisher | WFL Publisher |
| record_format | dspace |
| spelling | upm.eprints-157932018-03-30T03:39:59Z http://psasir.upm.edu.my/id/eprint/15793/ Neuro-fuzzy modeling of a conveyor-belt grain dryer Lutfy, Omar F. Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Abbas, Kassim Ali Mansor, Hasmah The grain drying process is one of the most critical post-harvest operations in modern agricultural production. Development of a reliable control strategy for this process plays an important role in improving the overall efficiency and productivity of the drying process. In control system design, the first problem to be addressed is the availability of a relatively simple and accurate model of the process to be controlled. However, the majority of the models developed for the grain drying process and the numerical methods required to solve them are characterized by their highly complex nature, and thus they are not suitable to be utilized in control system design. This paper presents an application of a neuro-fuzzy system, in particular the adaptive neuro-fuzzy inference system (ANFIS), to develop a data-driven model for a conveyor-belt grain dryer. This model can be easily used in control system design to develop a reliable control strategy for the drying process. By conducting a real-time experiment to dry paddy grains, a set of input-output data were collected from a laboratory-scale conveyor-belt grain dryer. These data were then presented to the ANFIS network in order to learn the nonlinear functional relationship between the input and output data by this network. Based on utilizing a clustering method to identify the structure of the ANFIS network, the resulting ANFIS model has shown a remarkable modeling performance to represent the drying process. In addition, the modeling result achieved by this ANFIS model was compared with those of an autoregressive with exogenous input (ARX) model and an artificial neural network (ANN) model, and the results clearly showed the superiority of the ANFIS model. WFL Publisher 2010 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/15793/1/Neuro.pdf Lutfy, Omar F. and Mohd Noor, Samsul Bahari and Marhaban, Mohammad Hamiruce and Abbas, Kassim Ali and Mansor, Hasmah (2010) Neuro-fuzzy modeling of a conveyor-belt grain dryer. Journal of Food, Agriculture and Environment, 8 (3&4). pp. 128-134. ISSN 1459-0255; ESSN: 1459-0263 https://www.wflpublisher.com/Abstract/2980 10.1234/4.2010.2980 |
| spellingShingle | Lutfy, Omar F. Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Abbas, Kassim Ali Mansor, Hasmah Neuro-fuzzy modeling of a conveyor-belt grain dryer |
| title | Neuro-fuzzy modeling of a conveyor-belt grain dryer |
| title_full | Neuro-fuzzy modeling of a conveyor-belt grain dryer |
| title_fullStr | Neuro-fuzzy modeling of a conveyor-belt grain dryer |
| title_full_unstemmed | Neuro-fuzzy modeling of a conveyor-belt grain dryer |
| title_short | Neuro-fuzzy modeling of a conveyor-belt grain dryer |
| title_sort | neuro fuzzy modeling of a conveyor belt grain dryer |
| url | http://psasir.upm.edu.my/id/eprint/15793/1/Neuro.pdf |
| work_keys_str_mv | AT lutfyomarf neurofuzzymodelingofaconveyorbeltgraindryer AT mohdnoorsamsulbahari neurofuzzymodelingofaconveyorbeltgraindryer AT marhabanmohammadhamiruce neurofuzzymodelingofaconveyorbeltgraindryer AT abbaskassimali neurofuzzymodelingofaconveyorbeltgraindryer AT mansorhasmah neurofuzzymodelingofaconveyorbeltgraindryer |