Neural network analysis of switchability of microstrip rectangular patch antenna printed on ferrite material

✍ Scribed by Naveen Kumar Saxena; Mohd. Ayub Khan; P. K. S. Pourush; Nitendar Kumar

• Publisher: John Wiley and Sons
• Year: 2009
• Tongue: English
• Weight: 204 KB
• Volume: 20
• Category: Article
• ISSN: 1096-4290
• DOI: 10.1002/mmce.20386

✦ Synopsis

A switchable microstrip rectangular patch antenna printed on ferrite substrate in the X-band is presented using general artificial neural network (ANN) analysis. The ferrite substrate offers a number of unique radiation characteristics including switchable and polarized radiations from a microstrip antenna with DC magnetic biasing. In such a case, for particular frequency most of the power is converted into magnetostatic waves and little radiates into air. Subsequently, the antenna behaves as switch off, in the sense that it effectively absent as radiator. Both synthesis and analysis are mainly focused on the switchability of antenna. In this work, radial basis function (RBF) networks are used in ANN models. Synthesis is defined as the forward side and then analysis as the reverse side of the problem. Here, the analysis is considered as a final stage of the design procedure, therefore, the parameters of the analysis ANN network are determined by the data obtained reversing the input-output data of the synthesis network. In the RBF network, the spread value was chosen as 0.01, which gives the best accuracy. RBF is tested with 100 sample frequencies but trained only for particular cutoff 15 sample frequencies. V