论文标题
基于神经网络的非线性观察员
Neural Network Based Nonlinear Observers
论文作者
论文摘要
讨论了基于众所周知的最低能量估计概念的非线性观察者。该方法依赖于由汉密尔顿 - 雅各比 - 贝尔曼方程确定的输出注入操作员，随后由神经网络近似。提出了一个合适的优化问题，允许学习网络参数，并针对线性和非线性振荡器进行了数值研究。
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.
