A new neural network technique for the design of multilayered microwave shielded bandpass filters

Juan Pascual García, Fernando Quesada Pereira, David Cañete Rebenaque, Juan Sebastian Gomez Diaz, Alejandro Álvarez Melcón

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In this work, we propose a novel technique based on neural networks, for the design of microwave filters in shielded printed technology. The technique uses radial basis function neural networks to represent the non linear relations between the quality factors and coupling coefficients, with the geometrical dimensions of the resonators. The radial basis function neural networks are employed for the first time in the design task of shielded printed filters, and permit a fast and precise operation with only a limited set of training data. Thanks to a new cascade configuration, a set of two neural networks provide the dimensions of the complete filter in a fast and accurate way. To improve the calculation of the geometrical dimensions, the neural networks can take as inputs both electrical parameters and physical dimensions computed by other neural networks. The neural network technique is combined with gradient based optimization methods to further improve the response of the filters. Results are presented to demonstrate the usefulness of the proposed technique for the design of practical microwave printed coupled line and hairpin filters.

Original languageEnglish (US)
Pages (from-to)405-415
Number of pages11
JournalInternational Journal of RF and Microwave Computer-Aided Engineering
Issue number3
StatePublished - May 1 2009
Externally publishedYes


  • Filter design
  • Microwave filters
  • Multilayered shielded structures
  • Neural networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering


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