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Estimating Scalable Common-Denominator Laplace-Domain MIMO Models in an Errors-in-Variables Framework

机译:在变量误差框架中估计可伸缩共母拉普拉斯域MIMO模型

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Design of electrical systems demands simulations using models evaluated in different design parameters choices. To enable the simulation of linear systems, one often requires their modeling as ordinary differential equations given tabular data obtained from device simulations or measurements. Existing techniques need to do this for every choice of design parameters since the model representations dont scale smoothly with the external parameter. The paper describes a frequency-domain identification algorithm to extract the poles and zeros of linear MIMO systems. Furthermore, it expresses the poles and zeros as trajectories that are functions of the design parameter(s). The paper describes the used framework, solves the starting-value problem, presents a solution for high-order systems and provides a model-order selection strategy. The properties of the algorithm are illustrated on microwave measurements of inductors, a variable gain amplifier and a high-order SAW-filter. As shown by these examples,the proposed identification algorithm is very well suited to derive scalable, physically relevant models out of tabular frequency-response data.
机译:电气系统的设计需要使用在不同设计参数选择中评估的模型进行仿真。为了能够进行线性系统的仿真,通常需要给定从设备仿真或测量获得的表格数据,将它们建模为常微分方程。现有的技术需要对设计参数的每种选择进行此操作,因为模型表示不能随外部参数顺利缩放。本文描述了一种频域识别算法,用于提取线性MIMO系统的极点和零点。此外,它将极点和零点表示为轨迹,这些轨迹是设计参数的函数。本文描述了所使用的框架,解决了起始值问题,提出了高阶系统的解决方案并提供了模型阶选择策略。该算法的性质在电感器,可变增益放大器和高阶SAW滤波器的微波测量中得到了说明。如这些示例所示,所提出的识别算法非常适合从表格频率响应数据中得出可扩展的,物理相关的模型。

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