深度KKL：数据驱动的非线性系统输出预测
Deep KKL: Data-driven Output Prediction for Non-Linear Systems
摘要：
我们解决了输出预测的问题，即。设计一个自治非线性系统的模型，能够预测其未来的观测。我们首先定义了一个一般框架，汇集了必要的属性，这样的输出预测的发展。特别是，我们从控制理论和数据驱动技术（机器学习）两个不同的角度来看待这个问题，并试图以一致的方式来制定它，减少两个领域之间的差距。在此基础上制定和问题的定义，我们提出了一个预测结构的基础上Kazantzis-Kravaris/Luenberger（KKL）的观察员，我们表明，KKL适合我们的一般框架。最后，我们提出了一个建设性的解决方案，这个预测，仅仅依赖于一个小的一组从系统测量的轨迹。我们的实验表明，我们的解决方案可以获得一个有效的预测在一个子集的观察空间。
英文摘要：
We address the problem of output prediction, ie. designing a model for autonomous nonlinear systems capable of forecasting their future observations. We first define a general framework bringing together the necessary properties for the development of such an output predictor. In particular, we look at this problem from two different viewpoints, control theory and data-driven techniques (machine learning), and try to formulate it in a consistent way, reducing the gap between the two fields. Building on this formulation and problem definition, we propose a predictor structure based on the Kazantzis-Kravaris/Luenberger (KKL) observer and we show that KKL fits well into our general framework. Finally, we propose a constructive solution for this predictor that solely relies on a small set of trajectories measured from the system. Our experiments show that our solution allows to obtain an efficient predictor over a subset of the observation space.
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。
