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首页> 外文会议>59th ARFTG (Automatic RF Techniques Group) Conference Digest, Jun 7, 2002, Seattle, Washington >Artificial Neural Network Model for HEMTs Constructed from Large-Signal Time-Domain Measurements
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Artificial Neural Network Model for HEMTs Constructed from Large-Signal Time-Domain Measurements

机译：基于大信号时域测量的HEMT人工神经网络模型

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摘要

A methodology to construct behavioural models for microwave devices from time-domain large-signal measurements has been modified by using artificial neural networks (ANNs) for the multivariate fitting functions instead of polynomials. The behavioural models for the class of devices (microwave transistors) considered can be defined by expressing the terminal currents as functions of the state variables, the embedded voltages. In this work, we show that ANNs are valuable candidates to represent these relationships. They outperform models based on multivariate polynomials, because they can better model the typical physical characteristics of the devices considered. Experimental results are quantitatively confirmed by using comparison metrics.
机译：通过使用人工神经网络（ANN）代替多项式来拟合多元拟合函数，从时域大信号测量中构建微波设备行为模型的方法已得到修改。可以通过将端子电流表示为状态变量，嵌入电压的函数来定义所考虑的设备（微波晶体管）类别的行为模型。在这项工作中，我们证明了人工神经网络是代表这些关系的有价值的候选者。它们优于基于多元多项式的模型，因为它们可以更好地对所考虑设备的典型物理特性进行建模。实验结果通过使用比较指标进行定量确认。

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