Analysis and Synthesis of Bioinspired Frequency Selective Surfaces Using Neural Networks for Applications in Wireless Communication Systems
Frequency selective surface, bioinspired FSS, four leaf clover, maple leaf, synthesis, artificial neural networks, Bayesian regularization.
This work describes the analysis of Bioinspired Frequency Selective Surfaces (BFSS) for applications in wireless systems, operating in the C band, Ku band and UWB (ultra-wideband). Simple and coupled BFSS structures are considered. The BFSSs have array elements with the four-leaf clover-shaped and maple leaf-shaped, and presented dual-band response, with the operating frequencies in the C and Ku bands. For the development of BFSSs with the ultra-wideband, a cascade structure was developed, in which a FSS with elements with patches with the shape of square loops was coupled to the BFSSs. In addition, the four-leaf clover BFSS synthesis was developed using an artificial neural network with a cascade feedforward architecture and Bayesian regularization training algorithm to obtain the specifications of resonance frequency and respective desired bandwidths. The numerical values obtained by simulations for the developed prototypes were obtained by the ANSYS HFSS software. Prototypes were manufactured and experimentally characterized. The measured results were compared with the simulated ones and a good agreement was observed.