Process fault detection and diagnosis using a dynamic neural networks model
Recently, neural networks has generated considerable interest as an alternative non-linear modelling tool. The major attraction is the learning capabilities of neural networks, and the fact that multi-layer, feed forward networks can approximate any non-linear function with arbitrary accuracy. This...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Universiti Putra Malaysia Press
2002
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| Online Access: | http://psasir.upm.edu.my/id/eprint/33839/ http://psasir.upm.edu.my/id/eprint/33839/1/33839.pdf |
| _version_ | 1848847609201950720 |
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| author | Abdul Rahman, Ribhan Zafira Che Soh, Azura Ahmad, Erny Arniza Mohd Noor, Samsul Bahari Gomm, J. B. |
| author_facet | Abdul Rahman, Ribhan Zafira Che Soh, Azura Ahmad, Erny Arniza Mohd Noor, Samsul Bahari Gomm, J. B. |
| author_sort | Abdul Rahman, Ribhan Zafira |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Recently, neural networks has generated considerable interest as an alternative non-linear modelling tool.
The major attraction is the learning capabilities of neural networks, and the fact that multi-layer, feed forward networks can approximate any non-linear function with arbitrary accuracy. This study describes the application of the multi-layer perceptron (MLP) neural network, trained using back-error propagation, to obtain a representative model of a non-linear process over a wide operational region. The purpose of this study is mainly to investigate the use of dynamic neural networks model for fault detection and diagnosis of the process control. The MATLAB with SIMULINK process and Multi-Layer Perceptron Software Package is used as a method to procure the required result. |
| first_indexed | 2025-11-15T09:21:19Z |
| format | Conference or Workshop Item |
| id | upm-33839 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:21:19Z |
| publishDate | 2002 |
| publisher | Universiti Putra Malaysia Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-338392018-03-30T03:04:26Z http://psasir.upm.edu.my/id/eprint/33839/ Process fault detection and diagnosis using a dynamic neural networks model Abdul Rahman, Ribhan Zafira Che Soh, Azura Ahmad, Erny Arniza Mohd Noor, Samsul Bahari Gomm, J. B. Recently, neural networks has generated considerable interest as an alternative non-linear modelling tool. The major attraction is the learning capabilities of neural networks, and the fact that multi-layer, feed forward networks can approximate any non-linear function with arbitrary accuracy. This study describes the application of the multi-layer perceptron (MLP) neural network, trained using back-error propagation, to obtain a representative model of a non-linear process over a wide operational region. The purpose of this study is mainly to investigate the use of dynamic neural networks model for fault detection and diagnosis of the process control. The MATLAB with SIMULINK process and Multi-Layer Perceptron Software Package is used as a method to procure the required result. Universiti Putra Malaysia Press 2002 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/33839/1/33839.pdf Abdul Rahman, Ribhan Zafira and Che Soh, Azura and Ahmad, Erny Arniza and Mohd Noor, Samsul Bahari and Gomm, J. B. (2002) Process fault detection and diagnosis using a dynamic neural networks model. In: 2nd World Engineering Congress, 22-25 July 2002, Sarawak, Malaysia. (pp. 401-406). |
| spellingShingle | Abdul Rahman, Ribhan Zafira Che Soh, Azura Ahmad, Erny Arniza Mohd Noor, Samsul Bahari Gomm, J. B. Process fault detection and diagnosis using a dynamic neural networks model |
| title | Process fault detection and diagnosis using a dynamic neural networks model |
| title_full | Process fault detection and diagnosis using a dynamic neural networks model |
| title_fullStr | Process fault detection and diagnosis using a dynamic neural networks model |
| title_full_unstemmed | Process fault detection and diagnosis using a dynamic neural networks model |
| title_short | Process fault detection and diagnosis using a dynamic neural networks model |
| title_sort | process fault detection and diagnosis using a dynamic neural networks model |
| url | http://psasir.upm.edu.my/id/eprint/33839/ http://psasir.upm.edu.my/id/eprint/33839/1/33839.pdf |