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S-functions behavioral model order reduction based on narrowband modulated large-signal network analyzer measurements

机译:基于窄带调制大信号网络分析仪测量的S函数行为模型顺序减少

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In this paper we report for the first time on order reduction applied to S-functions behavioral models. The most dominant model parameters are selected based on the relative uncertainty of their estimated values evaluated against a threshold value. The selection procedure is performed on the same measurement data that is used to extract the model and obtained using a large-signal network analyzer. High level of model order reduction, achieved without any substantial loss of the prediction accuracy, is demonstrated on S-functions extracted for a packaged pHEMT device.
机译:在本文中,我们首次向S-Functions行为模型进行了第一次报告。基于对阈值评估的估计值的相对不确定性来选择最主导的模型参数。在用于提取模型并使用大信号网络分析器获得的相同测量数据上执行选择过程。在为封装的PHEMT装置提取的S函数上证明了在没有任何重大预测精度的情况下实现的高水平的模型顺序减少。

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