arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.07269math.OC
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基于神经网络的非线性观测器

Neural Network Based Nonlinear Observers

Tobias Breiten, Karl Kunisch

摘要:

讨论了基于最小能量估计的非线性观测器。该方法依赖于由Hamilton-Jacobi-Bellman方程确定的输出注入算子,随后由神经网络近似。一个合适的最优化问题,允许学习网络参数的线性和非线性振荡器提出和数值研究。

英文摘要:

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 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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