人工神经网络
反向
趋同(经济学)
计算机科学
反问题
算法
滤波器(信号处理)
局部最优
数学优化
数学
人工智能
几何学
数学分析
经济
计算机视觉
经济增长
作者
Hao Huang,Xiuye Liang,Fang Guan,Jian Zi
摘要
In this letter, we propose an end-to-end inverse modeling and optimization method for microwave filter designs based on the data-augmentation learning strategy. Because of the non-uniqueness of solutions, it is difficult to achieve good convergence with artificial neural networks for inverse designs when the parameter space is very large. We prove that the accuracy of inverse predictions can be significantly improved using the network's self-generated data and the optimization can be greatly accelerated with the help of the inverse network. The predicted structural parameters can be used as initial values for optimization, which reduces the number of iterations and avoids falling into local optima. This method is applied to designs of fourth-order interdigital cavity filters. The measurement and simulation agree well.
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