论文标题
自适应参数调整用于线性系统的可及性分析
Adaptive Parameter Tuning for Reachability Analysis of Linear Systems
论文作者
论文摘要
尽管有可能快速计算可及的大规模线性系统的集合，但从业者尚未广泛应用当前方法。造成这种情况的主要原因可能是当前方法不可推动按钮能力，并且仍然需要手动设置关键参数，例如时间步长尺寸和使用的设置表示的准确性 - 这些设置需要专业知识。我们提出了一个通用框架，可以自动找到近乎最佳的参数，以分析用户定义的精度，以分析线性系统。为了尽可能限制计算开销，我们的方法在运行时调节所有相关参数。我们评估了拱竞赛的基准以及随机示例的方法。我们的结果表明，我们的新框架比手动调整的参数更快地验证所选基准测试速度，并且与遗传算法相比，它的数量级快。
Despite the possibility to quickly compute reachable sets of large-scale linear systems, current methods are not yet widely applied by practitioners. The main reason for this is probably that current approaches are not push-button-capable and still require to manually set crucial parameters, such as time step sizes and the accuracy of the used set representation -- these settings require expert knowledge. We present a generic framework to automatically find near-optimal parameters for reachability analysis of linear systems given a user-defined accuracy. To limit the computational overhead as much as possible, our methods tune all relevant parameters during runtime. We evaluate our approach on benchmarks from the ARCH competition as well as on random examples. Our results show that our new framework verifies the selected benchmarks faster than manually-tuned parameters and is an order of magnitude faster compared to genetic algorithms.
