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Identification of a discrete-time dynamical system

机译:离散时间动力系统的辨识

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摘要

A novel generalized minimum variance (GMV) system identification algorithm is developed, and its performance is gauged against the established generalized least squares (GLS) estimation algorithm. The emphasis of the proposed GMV algorithm is on the rigorous treatment of measurement noise for dynamical system identification. A careful analysis of the measurement situation on hand yields a novel fixed-point calculation-based parameter estimation algorithm. The novel and established algorithms are compared in carefully performed and reproducible experiments which include measurement noise. Differences are apparent under small (measurement) sample operation, whereas under sufficient excitation, the algorithms produce statistically similar results
机译:开发了一种新颖的广义最小方差(GMV)系统识别算法,并针对已建立的广义最小二乘(GLS)估计算法对它的性能进行了评估。提出的GMV算法的重点在于对用于动态系统识别的测量噪声的严格处理。仔细分析手头的测量情况,得出了一种新颖的基于定点计算的参数估计算法。在经过仔细执行且可重现的实验(包括测量噪声)中比较了这种新颖的算法。在小(测量)样本操作下,差异显而易见,而在充分激发下,该算法产生统计上相似的结果

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