On the Neural Networks for the System Identification Applications
碩士 === 逢甲大學 === 航太與系統工程所 === 100 === In this thesis, the tasks of the NARX (Nonlinear Auto-Regressive with eXogenous) model identification are fulfilled by using the Neural Network theory with the Nonlinear Black-Box Model Identification algorithm of MATLAB’s System Identification Toolbox. For simul...
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2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/65015643767931488759 |
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ndltd-TW-100FCU052950132015-10-13T21:27:32Z http://ndltd.ncl.edu.tw/handle/65015643767931488759 On the Neural Networks for the System Identification Applications 類神經網路於系統識別之應用 Shen-Jie Chu 朱信傑 碩士 逢甲大學 航太與系統工程所 100 In this thesis, the tasks of the NARX (Nonlinear Auto-Regressive with eXogenous) model identification are fulfilled by using the Neural Network theory with the Nonlinear Black-Box Model Identification algorithm of MATLAB’s System Identification Toolbox. For simulation with system identification purpose, a mass-spring-damper system with single-input / single-output (SISO) and the longitudinal dynamics of an aircraft with multi-input / multi-output (MIMO) formulations will be used. The results of the suspension model show that if the black box model of the neural network is tuned properly, the modified model would not be affected by the noisy input/output, and achieve some adequate identification consequence. Chii-Chuan Chen 陳啟川 2012 學位論文 ; thesis 52 zh-TW |
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zh-TW |
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Others
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碩士 === 逢甲大學 === 航太與系統工程所 === 100 === In this thesis, the tasks of the NARX (Nonlinear Auto-Regressive with eXogenous) model identification are fulfilled by using the Neural Network theory with the Nonlinear Black-Box Model Identification algorithm of MATLAB’s System Identification Toolbox. For simulation with system identification purpose, a mass-spring-damper system with single-input / single-output (SISO) and the longitudinal dynamics of an aircraft with multi-input / multi-output (MIMO) formulations will be used. The results of the suspension model show that if the black box model of the neural network is tuned properly, the modified model would not be affected by the noisy input/output, and achieve some adequate identification consequence.
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| author2 |
Chii-Chuan Chen |
| author_facet |
Chii-Chuan Chen Shen-Jie Chu 朱信傑 |
| author |
Shen-Jie Chu 朱信傑 |
| spellingShingle |
Shen-Jie Chu 朱信傑 On the Neural Networks for the System Identification Applications |
| author_sort |
Shen-Jie Chu |
| title |
On the Neural Networks for the System Identification Applications |
| title_short |
On the Neural Networks for the System Identification Applications |
| title_full |
On the Neural Networks for the System Identification Applications |
| title_fullStr |
On the Neural Networks for the System Identification Applications |
| title_full_unstemmed |
On the Neural Networks for the System Identification Applications |
| title_sort |
on the neural networks for the system identification applications |
| publishDate |
2012 |
| url |
http://ndltd.ncl.edu.tw/handle/65015643767931488759 |
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AT shenjiechu ontheneuralnetworksforthesystemidentificationapplications AT zhūxìnjié ontheneuralnetworksforthesystemidentificationapplications AT shenjiechu lèishénjīngwǎnglùyúxìtǒngshíbiézhīyīngyòng AT zhūxìnjié lèishénjīngwǎnglùyúxìtǒngshíbiézhīyīngyòng |
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