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
본 논문에서는 아핀 및 일반 비선형 시스템에 대해 3층 신경망을 사용하여 Hamilton-Jacobi-Bellman 방정식의 해를 근사화하는 새로운 알고리즘을 제안하고, 폐루프 시스템을 차선책으로 만드는 상태 피드백 제어기를 얻을 수 있습니다. 제한된 훈련 영역 내에서. 행렬 미적분학 이론은 신경망의 가중치 매개변수 행렬에 대한 훈련 오류의 기울기를 얻는 데 사용됩니다. 패턴 모드 학습 알고리즘을 사용하여 많은 사례에서 제안한 방법의 효율성을 보여줍니다.
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부
Xu WANG, Kiyotaka SHIMIZU, "Approximate Solution of Hamilton-Jacobi-Bellman Equation by Using Neural Networks and Matrix Calculus Techniques" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 6, pp. 1549-1556, June 2001, doi: .
Abstract: In this paper we propose a new algorithm to approximate the solution of Hamilton-Jacobi-Bellman equation by using a three layer neural network for affine and general nonlinear systems, and the state feedback controller can be obtained which make the closed-loop systems be suboptimal within a restrictive training domain. Matrix calculus theory is used to get the gradients of training error with respect to the weight parameter matrices in neural networks. By using pattern mode learning algorithm, many examples show the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_6_1549/_p
부
@ARTICLE{e84-a_6_1549,
author={Xu WANG, Kiyotaka SHIMIZU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Approximate Solution of Hamilton-Jacobi-Bellman Equation by Using Neural Networks and Matrix Calculus Techniques},
year={2001},
volume={E84-A},
number={6},
pages={1549-1556},
abstract={In this paper we propose a new algorithm to approximate the solution of Hamilton-Jacobi-Bellman equation by using a three layer neural network for affine and general nonlinear systems, and the state feedback controller can be obtained which make the closed-loop systems be suboptimal within a restrictive training domain. Matrix calculus theory is used to get the gradients of training error with respect to the weight parameter matrices in neural networks. By using pattern mode learning algorithm, many examples show the effectiveness of the proposed method.},
keywords={},
doi={},
ISSN={},
month={June},}
부
TY - JOUR
TI - Approximate Solution of Hamilton-Jacobi-Bellman Equation by Using Neural Networks and Matrix Calculus Techniques
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1549
EP - 1556
AU - Xu WANG
AU - Kiyotaka SHIMIZU
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2001
AB - In this paper we propose a new algorithm to approximate the solution of Hamilton-Jacobi-Bellman equation by using a three layer neural network for affine and general nonlinear systems, and the state feedback controller can be obtained which make the closed-loop systems be suboptimal within a restrictive training domain. Matrix calculus theory is used to get the gradients of training error with respect to the weight parameter matrices in neural networks. By using pattern mode learning algorithm, many examples show the effectiveness of the proposed method.
ER -