Adaptive Minimum Variance Control of Stochastic Fuzzy T-S Modes

碩士 === 中華大學 === 電機工程學系(所) === 95 === Adaptive minimum variance control for stochastic T-S fuzzy ARMAX model is addressed in this study. From the fuzzy ARMAX model, a fuzzy one-step ahead prediction model is first developed. A stochastic gradient algorithm is then proposed to identify the parameters...

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Main Authors:	Chung-Hung Chiu, 邱政宏
Other Authors:	Bore. Kuen Lee
Format:	Others
Language:	en_US
Published:	2007
Online Access:	http://ndltd.ncl.edu.tw/handle/43721473263359126986

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spelling	ndltd-TW-095CHPI54420192016-05-18T04:12:22Z http://ndltd.ncl.edu.tw/handle/43721473263359126986 Adaptive Minimum Variance Control of Stochastic Fuzzy T-S Modes 隨機模糊T-S模式的適應最小變異控制 Chung-Hung Chiu 邱政宏碩士中華大學電機工程學系(所) 95 Adaptive minimum variance control for stochastic T-S fuzzy ARMAX model is addressed in this study. From the fuzzy ARMAX model, a fuzzy one-step ahead prediction model is first developed. A stochastic gradient algorithm is then proposed to identify the parameters of the related one-step-ahead predictor. Under the direct adaptive control scheme, minimum variance control is applied to find the control law to make the output track a desired reference signal. Stability and performance of the adaptive stochastic fuzzy control system are rigorously derived. Simulation study is also made to verify the developed results. Bore. Kuen Lee 李柏坤 2007 學位論文 ; thesis 56 en_US
collection	NDLTD
language	en_US
format	Others
sources	NDLTD
description	碩士 === 中華大學 === 電機工程學系(所) === 95 === Adaptive minimum variance control for stochastic T-S fuzzy ARMAX model is addressed in this study. From the fuzzy ARMAX model, a fuzzy one-step ahead prediction model is first developed. A stochastic gradient algorithm is then proposed to identify the parameters of the related one-step-ahead predictor. Under the direct adaptive control scheme, minimum variance control is applied to find the control law to make the output track a desired reference signal. Stability and performance of the adaptive stochastic fuzzy control system are rigorously derived. Simulation study is also made to verify the developed results.
author2	Bore. Kuen Lee
author_facet	Bore. Kuen Lee Chung-Hung Chiu 邱政宏
author	Chung-Hung Chiu 邱政宏
spellingShingle	Chung-Hung Chiu 邱政宏 Adaptive Minimum Variance Control of Stochastic Fuzzy T-S Modes
author_sort	Chung-Hung Chiu
title	Adaptive Minimum Variance Control of Stochastic Fuzzy T-S Modes
title_short	Adaptive Minimum Variance Control of Stochastic Fuzzy T-S Modes
title_full	Adaptive Minimum Variance Control of Stochastic Fuzzy T-S Modes
title_fullStr	Adaptive Minimum Variance Control of Stochastic Fuzzy T-S Modes
title_full_unstemmed	Adaptive Minimum Variance Control of Stochastic Fuzzy T-S Modes
title_sort	adaptive minimum variance control of stochastic fuzzy t-s modes
publishDate	2007
url	http://ndltd.ncl.edu.tw/handle/43721473263359126986
work_keys_str_mv	AT chunghungchiu adaptiveminimumvariancecontrolofstochasticfuzzytsmodes AT qiūzhènghóng adaptiveminimumvariancecontrolofstochasticfuzzytsmodes AT chunghungchiu suíjīmóhútsmóshìdeshìyīngzuìxiǎobiànyìkòngzhì AT qiūzhènghóng suíjīmóhútsmóshìdeshìyīngzuìxiǎobiànyìkòngzhì
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Adaptive Minimum Variance Control of Stochastic Fuzzy T-S Modes

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