Approximate Solution of Hamilton-Jacobi-Bellman Equation by Using Neural Networks and Matrix Calculus Techniques

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概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 2001-06-01

著者

• Wang Xu

  The Faculty Of Engineering Toyama University

• Shimizu Kiyotaka

  The Faculty Of Science And Technology Keio University

• Wang X

  The Faculty Of Science And Technology Keio University

関連論文

• A Method of Learning for Multi-Layer Networks
• Approximate Solution of Hamilton-Jacobi-Bellman Equation by Using Neural Networks and Matrix Calculus Techniques

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