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Polynomial modelling: Accuracy vs. shape

机译:多项式建模:精度与形状

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Behavioral modelling often aims in a simple black-box input-output response with minimal rms error with the used excitation. However, this may grossly overestimate the sensitivities to certain changes in the inputs, causing the model to be prone of big errors with a different excitation. This also makes it difficult to separate different nonlinear effects. In this paper we compare the effect of different model fitting strategies to the shape of the modeled I-V response and distortion contribution analysis, that very much relies on the fact, that besides accurate I-V match also the shape of the model and its higher derivatives are physically meaningful and correct.
机译:行为建模通常旨在简单的黑盒输入 - 输出响应,使用使用的激励最小的RMS误差。然而,这可能会严重高估对输入的某些变化的敏感性,导致模型易于具有不同激励的大错误。这也使得难以分离不同的非线性效应。在本文中,我们将不同的模型拟合策略对模型IV响应和失真贡献分析的形式进行了比较,这非常依赖于该事实,除了精确的IV匹配也是模型的形状及其更高的衍生物是物理的有意义和正确。

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