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Nonlinear Model Method of Microwave Power Device Based on Extreme Learning Machine

机译：基于极端学习机的微波功率器件非线性模型方法

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摘要

In large-signal simulations, building accurate models of power devices is essential for microwave circuit design. The traditional non-linear vector network measurement system is very complicated, and has the disadvantages of long test cycles and many measurement data. However, the powerful learning ability and accurate prediction ability of the extreme learning machine (ELM) neural network algorithm can be used to make up for the shortage of traditional measurement systems and improve the design efficiency. In this paper, an X-parameter model based on ELM neural network is established, the comparison of the model simulation with the actual circuit simulation shows that the fundamental error is within ±0.1 dBm and the second harmonic error is within ±0.3 dBm, which further verifies the correctness of the prediction model based on X-parameters of the ELM neural network.
机译：在大信号模拟中，建筑精确的电源设备模型对于微波电路设计至关重要。传统的非线性矢量网络测量系统非常复杂，具有长测试周期和许多测量数据的缺点。然而，极端学习机（ELM）神经网络算法的强大学习能力和准确的预测能力可用于弥补传统测量系统的短缺，提高设计效率。本文建立了一种基于ELM神经网络的X参数模型，与实际电路仿真的模型模拟的比较表明，基本误差在±0.1 dBm内，第二次谐波误差在±0.3 dBm内，基于ELM神经网络的X参数进一步验证预测模型的正确性。

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