空气板线馈电网络具有损耗小、耐功率高、幅相控制容易等特点而广泛应用于平面阵列天线中。
The air stripline feeding network is featured with low loss, high power capacitance, and it is easy to control its amplitude and phase.
本文将BP神经网络控制应用到静止式进相器中，取得良好的效果。
This paper applied BP neural network control to the Static Phase Generator, and achieved good results.
采用了神经网络PID自适应控制方法，改善了 非线性，降低了环境干扰的影响，提高了压电式 移 相器的性能。
Neural network PID adaptive control scheme was used to enhance the performance of piezoelectric phase shifter by improving the nonlinear performance and depressing the interference of environment.
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