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Passive macromodeling via mode-revealing transformation

机译:通过模式公开转换进行被动宏建模

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Direct application of model extraction methods to tabulated admittance data often gives a model which can suffer in accuracy when applied with high-impedance terminations. The problem is relevant in situations where the admittance matrix has a large eigenvalue ratio since the small eigenvalues are likely to become corrupted. We show a modeling procedure which alleviates the accuracy problem by introducing a mode-revealing transformation which is derived from the admittance eigenvector matrix. The transformed admittance matrix is subjected to model extraction and passivity enforcement by standard techniques, leading to a model which captures the full modal information of the transformed matrix and hence that of the original matrix. Finally, the model is transformed back to the original domain.
机译:将模型提取方法直接应用于列表的导纳数据通常会得到一个模型,该模型在与高阻抗端接一起应用时可能会出现精度问题。该问题在导纳矩阵具有较大特征值比的情况下很重要,因为较小的特征值可能会损坏。我们展示了一种建模程序,该程序通过引入从导纳特征向量矩阵导出的模式揭示变换来减轻精度问题。通过标准技术对转换后的导纳矩阵进行模型提取和无源性强制,从而得到一个模型,该模型捕获转换后矩阵的完整模态信息,从而捕获原始矩阵的全部模态信息。最后,模型被转换回原始域。

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