How Statistical Learning Can Help to Estimate the Number of Modes in Switched System Identification?

Louis Massucci; Fabien Lauer; Marion Gilson

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How Statistical Learning Can Help to Estimate the Number of Modes in Switched System Identification?

机译：统计学习如何有助于估计交换系统识别中的模式数量？

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This paper deals with hybrid dynamical system identification, and focuses more particularly on the estimation of the number of modes. An evaluation of a recent method based on model selection techniques from statistical learning is proposed, together with its comparison with more standard approaches based on algebraic arguments. Overall, three methods are benchmarked in various settings, including different noise conditions and data set sizes. The results provide insights into the respective advantages and weaknesses of the methods, thus yielding a set of guidelines on the choice of the most suitable method in a given situation for the practitioner.

机译：本文涉及混合动力动态系统识别，更专注于估计模式的数量。提出了一种基于统计学习的模型选择技术的最新方法的评估，其与基于代数参数的更多标准方法的比较。总体而言，三种方法在各种设置中是基准测试，包括不同的噪声条件和数据集大小。结果提供了对这些方法的各个优点和缺点的见解，从而产生了一套关于在医生在特定情况下选择最合适的方法的指导。

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《IFAC PapersOnLine》 |2021年第7期|共6页
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Louis Massucci; Fabien Lauer; Marion Gilson;
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How Statistical Learning Can Help to Estimate the Number of Modes in Switched System Identification?

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