The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kertas kerja ini membentangkan kaedah kawalan penyesuaian mudah (SAC) untuk sistem tak linear menggunakan rangkaian saraf berulang Elman (ERNN). Input kawalan diberikan oleh jumlah output pengawal penyesuaian mudah dan output ERNN. ERNN digunakan untuk mengimbangi ketaklinieran dinamik tumbuhan yang tidak diambil kira dalam SAC biasa. Peranan ERNN adalah untuk membina model linear dengan meminimumkan ralat output yang disebabkan oleh ketaklinearan dalam sistem kawalan.
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Salinan
Jianming LU, Jiunshian PHUAH, Takashi YAHAGI, "SAC for Nonlinear Systems Using Elman Recurrent Neural Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 8, pp. 1831-1840, August 2002, doi: .
Abstract: This paper presents a method of simple adaptive control (SAC) for nonlinear systems using Elman recurrent neural networks (ERNNs). The control input is given by the sum of the output of a simple adaptive controller and the output of the ERNN. The ERNN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual SAC. The role of the ERNN is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_8_1831/_p
Salinan
@ARTICLE{e85-a_8_1831,
author={Jianming LU, Jiunshian PHUAH, Takashi YAHAGI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={SAC for Nonlinear Systems Using Elman Recurrent Neural Networks},
year={2002},
volume={E85-A},
number={8},
pages={1831-1840},
abstract={This paper presents a method of simple adaptive control (SAC) for nonlinear systems using Elman recurrent neural networks (ERNNs). The control input is given by the sum of the output of a simple adaptive controller and the output of the ERNN. The ERNN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual SAC. The role of the ERNN is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.},
keywords={},
doi={},
ISSN={},
month={August},}
Salinan
TY - JOUR
TI - SAC for Nonlinear Systems Using Elman Recurrent Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1831
EP - 1840
AU - Jianming LU
AU - Jiunshian PHUAH
AU - Takashi YAHAGI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2002
AB - This paper presents a method of simple adaptive control (SAC) for nonlinear systems using Elman recurrent neural networks (ERNNs). The control input is given by the sum of the output of a simple adaptive controller and the output of the ERNN. The ERNN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual SAC. The role of the ERNN is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.
ER -