Efficient Generation of &lt;formula formulatype="inline"&gt; &lt;tex Notation="TeX"&gt;${\rm X}$&lt;/tex&gt;&lt;/formula&gt;-Parameters Transistor Models by Sequential Sampling

The nonlinear representation of active devices plays an important role in microwave circuit design. Whereas, it takes a long time to extract a large amount of large signal data, and the problem of memory resource and CPU occupancy becomes significant. In order to address the problems in traditional large-signal modeling methods, in this paper an X-parameter modeling method for microwave power devices based on extreme learning machine (ELM) is proposed. To demonstrate the effectiveness of this method, a double layer back propagation (BP) neural network model is established. Then, harmonic balance simulations are used to verify the accuracy of these two models. After comparisons, it is proved that the three harmonic errors of double layer BP neural network model are 9.525dBm, 1.309dBm and 14.593dBm, respectively, and the three harmonic errors of ELM model are 0.673 dBm, 0.314 dBm, 3.09 dBm, respectively. Furthermore, the three harmonic modulus errors of double layer BP neural network model are 0.031, 0.002, 7.665e-4, respectively, and the errors of ELM model are 0.005, 0.001, 8.38e-5, respectively. Finally, in order to verify the accuracy of the predicted model in circuit design, the predicted X-parameter is used in the design of power amplifier. Moreover, the errors of the double layer BP neural network prediction model at 2.5 GHz, 5 GHz and 7.5 GHz are 1.142 dBm, 1.436 dBm and 2.294 dBm, respectively. The output power error of the ELM model at 2.5 GHz, 5 GHz and 7.5 GHz are 0.089 dBm, 0.311 dBm and 0.309 dBm, respectively. These experimental results demonstrate that the established ELM model is an efficient and valid approach for modeling GaN high electron mobility transistor types of nonlinear microwave devices.

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Adaptive sampling method for experiments with fixed-grid variables in measurements of nonlinear active devices

Barmuta

Ferranti

Lewandowski

et al. 2015

2015 European Microwave Conference (EuMC)

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This paper describes an adaptive sampling method suitable for experiments where values of at least one of the input variables are limited to a fixed set. The method is evaluated on amplifier measurements using a calibrated nonlinear vector network analyzer. The input variables were the input power, varied in a continuous way, and the fundamental frequency, limited to sparse and dense sets of fixed values. The adaptive sampling algorithm enabled to sample more densely in the highly nonlinear regions, and evenly in the linear regions.

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Efficient Generation of <formula formulatype="inline"> <tex Notation="TeX">${\rm X}$</tex></formula>-Parameters Transistor Models by Sequential Sampling

Cited by 7 publications

References 9 publications

X‐parameter modelling of GaN HEMT based on neural network

X‐parameter modelling of GaN HEMT based on neural network

Approach for Extreme Learning Machine-Based Microwave Power Device Modeling

Adaptive sampling method for experiments with fixed-grid variables in measurements of nonlinear active devices

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