Artificial intelligence–based design optimization of nonuniform microstrip line band pass filter

Creative Commons License

Kuşkonmaz N. , Mahouti T., Yıldırım T.

International Journal Of Numerical Modelling-Electronic Networks Devices And Fields, vol.1, pp.1-13, 2021 (Journal Indexed in SCI)

  • Publication Type: Article / Article
  • Volume: 1
  • Publication Date: 2021
  • Doi Number: 10.1002/jnm.2888
  • Title of Journal : International Journal Of Numerical Modelling-Electronic Networks Devices And Fields
  • Page Numbers: pp.1-13


Abstract Design optimization of many electromagnetic and multiphysics problems have multiscale issues that require a fast, efficient, and accurate surrogate-based model to be used. Recently, in microwave engineering field, artificial intelligence–based models are being used for modeling of complex microwave stages. In all the studies, the main aim is to form models inner structure parameters, by using the given data to predict the linear/nonlinear relationships between given inputs and outputs. Herein, a surrogate-based model of a nonuniform microstrip transmission line (NTL) with a typical application of design optimization of a band-pass filter for ISM band application using deep learning (DL) and meta-heuristic optimization has been presented. In order to have a computationally efficient and accurate optimization process, firstly a 3D EM unit element model of NTL has been designed. The training and test data sets are created based on different sampling methods. A DL regression model modified multilayer perceptron M2LP have been used for prediction of scattering parameters (S) of the NTL, with respect to the variation of geometrical design parameters. The proposed S-parameters will then be used to calculate the equivalent S-parameters of the cascading NTL to be used to calculate the NTL-based microstrip band-pass filter S-parameter response. The optimal design parameters of each line used in the filter design have been determined using a fast and powerful optimization algorithm differential evolutionary algorithm.