Process Identification and Controller Tuning for a Recycle Plant Using Artificial Neural Networks

碩士 === 東海大學 === 化學工程學系 === 84 === Nonliear process identification and tuning of multivariable controlsystem for a recycle plant using artifcial neural networks (ANN) were studied in this thesis. A reactor/separator proc...

Full description

Bibliographic Details
Main Authors:	Shiau, Yu-Shi, 蕭玉璽
Other Authors:	Huang Chi-Tsung
Format:	Others
Language:	zh-TW
Published:	1996
Online Access:	http://ndltd.ncl.edu.tw/handle/69669907295271341084

id	ndltd-TW-084THU00063014
record_format	oai_dc
spelling	ndltd-TW-084THU000630142015-10-13T14:38:03Z http://ndltd.ncl.edu.tw/handle/69669907295271341084 Process Identification and Controller Tuning for a Recycle Plant Using Artificial Neural Networks 應用類神經網路於迴流工廠之程序識別與控制器調諧 Shiau, Yu-Shi 蕭玉璽碩士東海大學化學工程學系 84 Nonliear process identification and tuning of multivariable controlsystem for a recycle plant using artifcial neural networks (ANN) were studied in this thesis. A reactor/separator process was considered, and the process dynamics was identified using ANN under sinusoidal input in the set point of distillate controller. Weighting factors of ANN were obtained by general delta rule based on the least-squares criterion using NeuralWorks Professional II/PLUS software, and suitable ANN topologies were also verified by se veral testing data. The 'best'ANN model was then used for tuning the multivariable control systemof the recycle plant. Simulation results have demonstrated that ANN predicted model can provide a creditable performance. Huang Chi-Tsung 黃琦聰 1996 學位論文 ; thesis 105 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 東海大學 === 化學工程學系 === 84 === Nonliear process identification and tuning of multivariable controlsystem for a recycle plant using artifcial neural networks (ANN) were studied in this thesis. A reactor/separator process was considered, and the process dynamics was identified using ANN under sinusoidal input in the set point of distillate controller. Weighting factors of ANN were obtained by general delta rule based on the least-squares criterion using NeuralWorks Professional II/PLUS software, and suitable ANN topologies were also verified by se veral testing data. The 'best'ANN model was then used for tuning the multivariable control systemof the recycle plant. Simulation results have demonstrated that ANN predicted model can provide a creditable performance.
author2	Huang Chi-Tsung
author_facet	Huang Chi-Tsung Shiau, Yu-Shi 蕭玉璽
author	Shiau, Yu-Shi 蕭玉璽
spellingShingle	Shiau, Yu-Shi 蕭玉璽 Process Identification and Controller Tuning for a Recycle Plant Using Artificial Neural Networks
author_sort	Shiau, Yu-Shi
title	Process Identification and Controller Tuning for a Recycle Plant Using Artificial Neural Networks
title_short	Process Identification and Controller Tuning for a Recycle Plant Using Artificial Neural Networks
title_full	Process Identification and Controller Tuning for a Recycle Plant Using Artificial Neural Networks
title_fullStr	Process Identification and Controller Tuning for a Recycle Plant Using Artificial Neural Networks
title_full_unstemmed	Process Identification and Controller Tuning for a Recycle Plant Using Artificial Neural Networks
title_sort	process identification and controller tuning for a recycle plant using artificial neural networks
publishDate	1996
url	http://ndltd.ncl.edu.tw/handle/69669907295271341084
work_keys_str_mv	AT shiauyushi processidentificationandcontrollertuningforarecycleplantusingartificialneuralnetworks AT xiāoyùxǐ processidentificationandcontrollertuningforarecycleplantusingartificialneuralnetworks AT shiauyushi yīngyònglèishénjīngwǎnglùyúhuíliúgōngchǎngzhīchéngxùshíbiéyǔkòngzhìqìdiàoxié AT xiāoyùxǐ yīngyònglèishénjīngwǎnglùyúhuíliúgōngchǎngzhīchéngxùshíbiéyǔkòngzhìqìdiàoxié
_version_	1717754494213685248

Process Identification and Controller Tuning for a Recycle Plant Using Artificial Neural Networks

Similar Items