Westwick D T, Kearney R E
Med Biol Eng Comput. 1997 Mar;35(2):83-90. doi: 10.1007/BF02534135.
The identification of non-parametric impulse response functions (IRFs) from noisy finite-length data records is analysed using the techniques of matrix perturbation theory. Based on these findings, a method for IRF estimation is developed that is more robust than existing techniques, particularly when the input is non-white. Furthermore, methods are developed for computing confidence bounds on the resulting IRF estimates. Monte Carlo simulations are used to assess the capabilities of this new method and to demonstrate its superiority over classical techniques. An application to the identification of dynamic ankle stiffness in humans is presented.
利用矩阵扰动理论的技术，分析了从有噪声的有限长度数据记录中识别非参数脉冲响应函数（IRF）的问题。基于这些发现，开发了一种比现有技术更稳健的IRF估计方法，特别是当输入不是白噪声时。此外，还开发了用于计算所得IRF估计的置信区间的方法。使用蒙特卡罗模拟来评估这种新方法的性能，并证明其优于经典技术。还介绍了该方法在识别人体动态踝关节刚度方面的应用。
