A Neural Networks Approach to Dynamic Inverse Optimization Problems

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  • ニューラルネットワーク学習による動的逆最適化問題の解法
  • ニューラル ネットワーク ガクシュウ ニ ヨル ドウテキ ギャクサイテキカ モンダイ ノ カイホウ

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Abstract

In this paper, we propose a novel approach to dynamic inverse optimization problems by the learning of neural networks. Dynamic inverse optimization problems here mean to estimate a criterion function under which given sequences of input and output of a dynamical system are optimal. A neural network architecture representing the optimality condition including an algebraic Riccati equation is proposed for solving dynamic inverse optimization problems. Applications of the proposed method to observed input and output sequences well demonstrate its effectiveness.

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