Huebner W P, Saidel G M, Leigh R J
Department of Biomedical Engineering, Case Western Reserve University, University Hospitals, Cleveland, OH 44106.
Biol Cybern. 1990;62(4):265-73. doi: 10.1007/BF00201441.
We present a procedure that optimally adjusts specified parameters of a mathematical model to describe a set of measured data. The technique integrates a dynamic systems-simulation language with a robust algorithm for nonlinear parameter estimation, and it can be implemented on a microcomputer. Sensitivity functions are generated that indicate how the operation of the model is affected by each updated parameter. This procedure offers a greater resolution of optimal parameter values than other, less rigorous methods. To illustrate this technique we have applied it to the model of human smooth pursuit eye movements proposed by D.A. Robinson and colleagues (1986).
我们提出了一种程序，该程序可对数学模型的指定参数进行优化调整，以描述一组测量数据。该技术将动态系统仿真语言与用于非线性参数估计的强大算法相结合，并且可以在微型计算机上实现。生成了灵敏度函数，这些函数表明模型的运行如何受到每个更新参数的影响。与其他不太严格的方法相比，该程序能更精确地确定最优参数值。为了说明该技术，我们已将其应用于D.A.罗宾逊及其同事（1986年）提出的人类平稳跟踪眼球运动模型。
