Open AccessStudy of resonant microstrip antennas on artificial neural networks
Author(s) -
Gauri Shankar,
Manish Kumar,
Bijendra Mohan
Publication year - 2020
Publication title -
international journal on integrated education
Language(s) - English
Resource type - Journals
eISSN - 2620-3502
pISSN - 2615-3785
DOI - 10.31149/ijie.v3i9.623
Subject(s) - microstrip , bandwidth (computing) , microstrip antenna , computer science , artificial neural network , electronic engineering , backpropagation , transformation (genetics) , acoustics , materials science , engineering , telecommunications , physics , antenna (radio) , artificial intelligence , biochemistry , chemistry , gene
This paper presents a new model based on the backpropagation multilayered perception network to find accurately the bandwidth of both electrically thin and thick rectangular microstrip antennas. This proposed neural model does not require complicated Green's function methods and integral transformation techniques. The method can be used for a wide range of substrate thickness and permittivities and is useful for the computer-aided design of microstrip antennas. The results obtained by using this new method are in conformity with those reported elsewhere. This method may find wide applications in high-frequency printed antennas, especially at the millimeter-wave frequency range.