Sensory Motor Performance Program, Rehabilitation Institute of Chicago, IL 60611, USA.
IEEE Trans Biomed Eng. 2012 Dec;59(12):3541-9. doi: 10.1109/TBME.2012.2220767. Epub 2012 Sep 28.
System identification of physiological systems poses unique challenges, especially when the structure of the system under study is uncertain. Nonparametric techniques can be useful for identifying system structure, but these typically assume stationarity and require large amounts of data. Both of these requirements are often not easily obtained in the study of physiological systems. Ensemble methods for time-varying nonparametric estimation have been developed to address the issue of stationarity, but these require an amount of data that can be prohibitive for many experimental systems. To address this issue, we developed a novel algorithm that uses multiple short data segments. Using simulation studies, we showed that this algorithm produces system estimates with lower variability than previous methods when limited data are present. Furthermore, we showed that the new algorithm generates time-varying system estimates with lower total error than an ensemble method. Thus, this algorithm is well suited for the identification of physiological systems that vary with time or from which only short segments of stationary data can be collected.
生理系统的系统辨识带来了独特的挑战，特别是当研究中的系统结构不确定时。非参数技术可用于识别系统结构，但这些技术通常假设系统是平稳的，并且需要大量的数据。在生理系统的研究中，这两个要求通常都不容易获得。针对平稳性问题，已经开发了用于时变非参数估计的集合方法，但这些方法需要大量的数据，对于许多实验系统来说可能是不可行的。为了解决这个问题，我们开发了一种新的算法，该算法使用多个短数据段。通过仿真研究，我们表明，当数据有限时，该算法产生的系统估计具有比以前的方法更低的可变性。此外，我们还表明，新算法生成的时变系统估计的总误差低于集合方法。因此，该算法非常适合于随时间变化或只能收集短段平稳数据的生理系统的辨识。
