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 and 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.

中文翻译：

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基于神经网络的非线性观测器

讨论了基于众所周知的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，随后由神经网络逼近。提出了一个允许学习网络参数的合适优化问题，并针对线性和非线性振荡器进行了数值研究。