当前位置: X-MOL 学术Syst. Control Lett. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Neural network based nonlinear observers
Systems & Control Letters ( IF 2.1 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.sysconle.2020.104829
Tobias Breiten , Karl Kunisch

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:

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。
更新日期:2021-02-01
down
wechat
bug