Linearisation of process models : an analysis and applications using neural networks
The emergence of Artificial Neural Networks (ANNs) has rekindled interest in nonlinear control theory. Some applications of Artificial Neural Networks to process control have been reported in the literature. The capability of ANN is that even with an inappropriate choice of input variables, ANN can...
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| Format: | Thesis |
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2010
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| Online Access: | http://hdl.handle.net/10356/38977 |
| _version_ | 1826113872523689984 |
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| author | Fazlur Rahman M. H. R. |
| author2 | Zhu, Kuanyi |
| author_facet | Zhu, Kuanyi Fazlur Rahman M. H. R. |
| author_sort | Fazlur Rahman M. H. R. |
| collection | NTU |
| description | The emergence of Artificial Neural Networks (ANNs) has rekindled interest in nonlinear control theory. Some applications of Artificial Neural Networks to process control have been reported in the literature. The capability of ANN is that even with an inappropriate choice of input variables, ANN can be trained in such a way that many of the input variables may have little effect on the output. In such cases, the importance of knowledge of the process to be modelled cannot be overemphasised. A good understanding of the nature of the nonlinearity of process is important for proper application and exploitation of ANN for modelling and control. |
| first_indexed | 2024-10-01T03:30:19Z |
| format | Thesis |
| id | ntu-10356/38977 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T03:30:19Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/389772023-07-04T15:26:58Z Linearisation of process models : an analysis and applications using neural networks Fazlur Rahman M. H. R. Zhu, Kuanyi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems The emergence of Artificial Neural Networks (ANNs) has rekindled interest in nonlinear control theory. Some applications of Artificial Neural Networks to process control have been reported in the literature. The capability of ANN is that even with an inappropriate choice of input variables, ANN can be trained in such a way that many of the input variables may have little effect on the output. In such cases, the importance of knowledge of the process to be modelled cannot be overemphasised. A good understanding of the nature of the nonlinearity of process is important for proper application and exploitation of ANN for modelling and control. Doctor of Philosophy (EEE) 2010-05-21T03:38:20Z 2010-05-21T03:38:20Z 1997 1997 Thesis http://hdl.handle.net/10356/38977 NANYANG TECHNOLOGICAL UNIVERSITY 305 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Fazlur Rahman M. H. R. Linearisation of process models : an analysis and applications using neural networks |
| title | Linearisation of process models : an analysis and applications using neural networks |
| title_full | Linearisation of process models : an analysis and applications using neural networks |
| title_fullStr | Linearisation of process models : an analysis and applications using neural networks |
| title_full_unstemmed | Linearisation of process models : an analysis and applications using neural networks |
| title_short | Linearisation of process models : an analysis and applications using neural networks |
| title_sort | linearisation of process models an analysis and applications using neural networks |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | http://hdl.handle.net/10356/38977 |
| work_keys_str_mv | AT fazlurrahmanmhr linearisationofprocessmodelsananalysisandapplicationsusingneuralnetworks |