论文标题
可解释的机器学习控制 - 强大的控制和稳定性分析
Explainable Machine Learning Control -- robust control and stability analysis
论文作者
论文摘要
最近，可解释的AI一词被称为一种从人工智能中产生模型的方法，该方法允许解释。由于很长一段时间以来，使用的符号回归模型是完全可以解释和数学上的：在此贡献中，我们演示了如何使用符号回归方法来推断一个或几个优化标准或成本函数的动态系统的最佳控制。在以前的出版物中，通过使用遗传编程自动化机器学习控制来实现网络控制。在这里，我们关注对机器学习产生的分析表达式的后续分析。特别是，我们使用自动来分析作为我们模型的受控振荡器系统的稳定性。结果，我们表明，与访问较低的神经网络相比，可解释模型具有相当大的优势。
Recently, the term explainable AI became known as an approach to produce models from artificial intelligence which allow interpretation. Since a long time, there are models of symbolic regression in use that are perfectly explainable and mathematically tractable: in this contribution we demonstrate how to use symbolic regression methods to infer the optimal control of a dynamical system given one or several optimization criteria, or cost functions. In previous publications, network control was achieved by automatized machine learning control using genetic programming. Here, we focus on the subsequent analysis of the analytical expressions which result from the machine learning. In particular, we use AUTO to analyze the stability properties of the controlled oscillator system which served as our model. As a result, we show that there is a considerable advantage of explainable models over less accessible neural networks.
