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High-Dimensional Parameterized Macromodeling with Guaranteed Stability

机译:高维参数化宏观调,保证稳定性

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We introduce a Radial Basis Function (RBF) parameterized macromodeling algorithm, specifically designed for high-dimensional parameters. As opposed to standard approaches, the adopted RBF model representation has the potential to scale very favorably when the number of model parameters increases, since the number of model coefficients is not related to the dimension of the embedding parameter space. A transmission-line example with up to seven parameters is used to demonstrate the proposed approach.
机译:我们介绍了径向基函数(RBF)参数化的宏观解调算法,专门为高维参数设计。与标准方法相反,所采用的RBF模型表示具有在模型参数的数量增加时非常有利地规模,因为模型系数的数量与嵌入参数空间的维度无关。使用多达七个参数的传输线示例用于展示所提出的方法。

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