End‐to‐end inverse modeling and optimization for filters based on artificial neural network. Issue 11 (17th August 2022)

• Record Type: Journal Article
• Authors: Huang, Hao; Liang, Xiuye; Guan, Fang; Zi, Jian
• Is Part Of: International journal of RF and microwave computer-aided engineering. Volume 32:Issue 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-17
• Subjects: artificial neural network -- microwave filter -- optimization algorithm; Microwave devices -- Computer-aided design -- Periodicals; Computer-aided engineering -- Periodicals; 621.3813
• DOI: 10.1002/mmce.23365
• Languages: English
• ISSNs: 1096-4290

Abstract

Abstract: 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.