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Neural network based nonlinear observers

机译:基于神经网络的非线性观察者

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摘要

Nonlinear observers based on the well-known concept of minimum energy estimation are discussed. The approach relies on an output injection operator determined by a Hamilton-Jacobi-Bellman equation whose solution is subsequently approximated by a neural network. A suitable optimization problem allowing to learn the network parameters is proposed and numerically investigated for linear and nonlinear oscillators. (C) 2020 Elsevier B.V. All rights reserved.
机译:讨论了基于最小能量估计概念的非线性观测器。该方法依赖于由Hamilton-Jacobi-Bellman方程确定的输出注入算子,其解随后由神经网络近似。针对线性和非线性振荡器,提出了一个允许学习网络参数的优化问题,并进行了数值研究。(C) 2020爱思唯尔B.V.版权所有。

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