Sign-Perturbed Sums (SPS) is a finite sample system identification method that constructs exact, non-asymptotic confidence regions for the unknown parameters of linear systems without using any knowledge about the disturbances except that they are symmetrically distributed. In the available literature, the theoretical properties of SPS have been investigated under the assumption that the order of the system model is known to the user. In this paper, we analyse the behaviour of SPS when the model assumed by the user does not match the data generation mechanism, and we propose a new SPS algorithm able to detect the circumstance that the model order is incorrect.

符号扰动和（SPS）是一种有限样本系统识别方法，该方法针对线性系统的未知参数构造精确的，非渐近的置信区域，而无需使用任何关于扰动的知识，除非它们是对称分布的。在现有文献中，在假设用户知道系统模型顺序的前提下，对SPS的理论特性进行了研究。在本文中，我们分析了当用户假设的模型与数据生成机制不匹配时SPS的行为，并提出了一种新的SPS算法，可以检测模型顺序不正确的情况。