JACIII Vol.22 No.6 pp. 924-932
doi: 10.20965/jaciii.2018.p0924


Microwave Filter Modeling and Intelligent Tuning

Shengbiao Wu, Weihua Cao, Min Wu, and Can Liu

School of Automation, China University of Geoscience
No. 388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

Corresponding author

March 27, 2018
July 13, 2018
October 20, 2018
microwave filter, Cauchy method, improved space mapping, surrogate model

Traditional filter tuning methods mainly entail tuning by electromagnetic simulation technology, which treats the tuned filter as an ideal model. However, the structure of the actual filter is relatively complex, and filter tuning becomes affected by the loss of resonant cavity, phase loading and high-order mode. In this study, to solve these problems, the tuning process was divided into four stages. First, the passband and suppression of the filter could be tuned to a reasonable range by using the phase attribute of the reflection characteristics. Secondly, the tuning model parameters (coupling matrix) were extracted by curve fitting and the improved Cauchy method. Thirdly, the tuning model of the actual filter was established by a complex neural network. Finally, the mapping relationship between the surrogate model and the actual tuning model was established by the improved space mapping algorithm. By optimizing the parameters of surrogate model, we quickly obtained the optimal position of the screws. The results of the tuning experiment with the eighth coaxial cavity filter revealed that the method had high accuracy and fast convergence speed.

Cite this article as:
S. Wu, W. Cao, M. Wu, and C. Liu, “Microwave Filter Modeling and Intelligent Tuning,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.6, pp. 924-932, 2018.
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Last updated on Nov. 20, 2018