基于并行神经网络的滤波器快速优化方法
Fast Filter Optimization Method Based on Neural Network
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摘要: 目前,设计和优化微带滤波器的方法是通过电磁仿真软件EM 进行的。该方法存在两个不足之处:一是拟优化变量的初始值需要人工猜测,但受制于人工经验,致使猜测值与最优值差别较大,很容易使得优化结果陷入局部最优;二是单次仿真时间长导致优化过程耗时很长[1] 。文中利用并行神经网络并结合矢量拟合建立了替代模型进而优化滤波器。为了验证方法的可行性,文中设计了微带发卡滤波器并进行仿真和实验测试,仿真和实验测试结果基本一致。文中方法克服了目前滤波器设计和优化方法的两个不足之处,为快速设计高性能滤波器提供了方法。Abstract: Currently, the method for designing and optimizing microstrip filters is based on electromagnetic simulation EM software.However, this method has two major drawbacks. First, the initial value of the optimization variable needs to be guessed manually, but due to human experience limitations, the guessed value may deviate greatly from the optimal value, leading to optimization results trapped in local optima. Second, the long simulation time per iteration results in a lengthy optimization process[1] .This paper proposes a proxy model using a parallel neural network and vector fitting to optimize the filter. To verify the credibility of this method, a microstrip filter is designed and tested via simulation and experiments, which showed consistent results. The proposed method overcomes the two major drawbacks of the current filter design and optimization methods, which provides a fast and efficient approach for designing high-performance filters.
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Keywords:
- neural network /
- coupling matrix /
- microwave filter /
- fast optimization
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