N- step ahead prediction using artificial neural networks
The main objective of this project is to demonstrate the effectiveness of the proposed approach for the n-step ahead prediction using neural networks, which are trained using real plant data.
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sg-ntu-dr.10356-31992023-07-04T15:10:11Z N- step ahead prediction using artificial neural networks Saravanan Solaiappan. Devanathan, Rajagopalan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering The main objective of this project is to demonstrate the effectiveness of the proposed approach for the n-step ahead prediction using neural networks, which are trained using real plant data. Master of Science (Computer Control and Automation) 2008-09-17T09:24:29Z 2008-09-17T09:24:29Z 2001 2001 Thesis http://hdl.handle.net/10356/3199 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Saravanan Solaiappan. N- step ahead prediction using artificial neural networks |
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The main objective of this project is to demonstrate the effectiveness of the proposed approach for the n-step ahead prediction using neural networks, which are trained using real plant data. |
author2 |
Devanathan, Rajagopalan |
author_facet |
Devanathan, Rajagopalan Saravanan Solaiappan. |
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Theses and Dissertations |
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Saravanan Solaiappan. |
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Saravanan Solaiappan. |
title |
N- step ahead prediction using artificial neural networks |
title_short |
N- step ahead prediction using artificial neural networks |
title_full |
N- step ahead prediction using artificial neural networks |
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N- step ahead prediction using artificial neural networks |
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N- step ahead prediction using artificial neural networks |
title_sort |
n- step ahead prediction using artificial neural networks |
publishDate |
2008 |
url |
http://hdl.handle.net/10356/3199 |
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1772827444728823808 |