Anderson Sean R, Dean Paul, Kadirkamanathan Visakan, Kaneko Chris R S, Porrill John
Neural Algorithms Research Group, Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, U.K.
IEEE Trans Biomed Eng. 2007 Dec;54(12):2205-13. doi: 10.1109/tbme.2007.896593.
System identification problems often arise where the only modeling records available consist of multiple short-time-duration signals. This motivates the development of a modeling approach that is tailored for this situation. An identification algorithm is presented here for parameter estimation based on minimizing the simulated prediction error, across multiple signals. The additional complexity of estimating the initial states corresponding to each signal is removed from the estimation algorithm. A numerical simulation demonstrates that the proposed algorithm performs well in comparison to the often-used least squares method (which leads to biased estimates when identifying systems from measurement noise corrupted signals). The approach is applied to the identification of the passive oculomotor plant; parameters are estimated that describe the dynamics of the plant, which represent the time constants of the visco-elastic elements that characterize the plant connective tissue.
系统识别问题经常出现在仅有的建模记录由多个短持续时间信号组成的情况下。这推动了一种针对这种情况量身定制的建模方法的发展。本文提出了一种识别算法，用于基于最小化多个信号上的模拟预测误差来进行参数估计。估计算法中消除了估计与每个信号对应的初始状态的额外复杂性。数值模拟表明，与常用的最小二乘法相比，该算法表现良好（当从受测量噪声污染的信号中识别系统时，最小二乘法会导致有偏差的估计）。该方法应用于被动动眼神经装置的识别；估计了描述该装置动态特性的参数，这些参数代表了表征该装置结缔组织的粘弹性元件的时间常数。
