论文标题
通过图群集开关切换线性动态系统的有效模型选择
Efficient model selection in switching linear dynamic systems by graph clustering
论文作者
论文摘要
开关KALMAN过滤器（SKF）所需的计算随系统操作模式的数量而呈指数增加。在本文中，为开关线性动态系统（SLDS）提出了一个可计算的图形表示，以及用于群集的最小和-MUM优化问题的解决方案，以在收集测量之前降低开关模式离线。结果表明，在完美的模式检测中，可以根据建议的图形结构中的差异度量准确量化由模式聚类引起的诱导误差。数值结果验证基于提出的框架的聚类有效地在给定模式检测的情况下有效地降低了模型的复杂性，并且如果满足基础假设，则可以很好地近似诱导的误差。
The computation required for a switching Kalman Filter (SKF) increases exponentially with the number of system operation modes. In this paper, a computationally tractable graph representation is proposed for a switching linear dynamic system (SLDS) along with the solution of a minimum-sum optimization problem for clustering to reduce the switching mode cardinality offline, before collecting measurements. It is shown that upon perfect mode detection, the induced error caused by mode clustering can be quantified exactly in terms of the dissimilarity measures in the proposed graph structure. Numerical results verify that clustering based on the proposed framework effectively reduces model complexity given uncertain mode detection and that the induced error can be well approximated if the underlying assumptions are satisfied.
