Nonlinear Equivalent Circuit Model Base on BPNN for GaN HEMTs

Xue, Manli; Sun, Li; Wang, Shuo; Liang, Peipei; Chen, Xiaolong; Nian, Fushun

doi:10.1109/edssc.2019.8754355

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2022

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“…A back propagation neural network (BPNN), one of the most widely known neural networks with a high learning accuracy [22], has been commonly used in fields, such as microwave device modeling and antenna design [23,24], and is expected to show its superiority in behavioral modeling describing the S-parameters of PAs at room temperature, as a function of frequency.…”

Section: Introductionmentioning

confidence: 99%

BPNN-Based Behavioral Modeling of the S-Parameter Variation Characteristics of PAs with Frequency at Different Temperatures

Zhao

Zhou

2022

Micromachines

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To address the issue of frequency nonlinearity modeling of RF PAs, which is rarely seen in the literature, a BPNN is applied to model the frequency nonlinearity of RF PAs in this paper. The BPNN is used to model the frequency nonlinearity of the RF PA, based on the actual measured S-parameter data at different ambient temperatures. The modeling results show that BPNN shows the advantage of a high accuracy in modeling the frequency nonlinearity of RF PAs. It is expected that a BPNN will also show the advantages of a high accuracy in the modeling process of other RF devices or circuits.

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Section: Introductionmentioning

confidence: 99%