Safety exploration using Gaussian process classification for uncertain systems
提出一种用高斯过程分类识别系统不确定参数空间中安全与不安全区域的方法，通过主动学习提高预测精度，帮助工程师判断系统是否满足性能要求。
In this paper, a novel method for identifying safe and unsafe regions of the system’s uncertain parameter space is proposed. For a given set of performance requirements, such estimation can be obtained by means of binary classification in which uncertain parameters are classified as either safe or unsafe in the sense that the given performance requirements are met or not. Hence, using Gaussian process classification it is possible to obtain (non-convex) safe and unsafe regions supported by minimum confidence levels of the corresponding estimations. We adopt active learning to update the Gaussian process classification model and to make more accurate predictions by selecting informative observations sequentially. The effectiveness of the proposed algorithm is demonstrated on various illustrative examples.