Computational Intelligence Techniques Application for Multiband and/or Broadband Frequency Selective Surface for Commercial Applications
Frequency Selective Surface; Ultrawideband; Multiband response; Comercial Application; Artificial Neural Network.
Frequency Selective Surface (FSS) have an important role in telecommunications due to technological advances in order to meet the requirement of users every day seeking technologies that achieve ever-higher transmission rates and are multifunctional. One of the alternatives found were the appearances of UWB technology and multiband technology, which ensures that telecommunications networks operating in several frequency bands reducing operating and system deployment costs. Given these requirements, we seek for FSS that have this type of behavior. To find FSS with such characteristics, one can make use of computational intelligence techniques in order to achieve optimal structures. Among the techniques, it has artificial neural networks, which will be object of study in this work in order to get a FSS with multiband response applied to ISM and UNII bands. A comparison will be made between MLP networks trained with backpropagation algorithm and RBF network.