Estimation of radiation characteristics of different slotted microstrip antennas using a knowledge-based neural networks model
In this article, a common neural model incorporated with prior knowledge is suggested for estimating radiation characteristics (i.e., resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies) of four-slotted microstrip antennas with inserted air-gap for dual-frequency operation. By incorporating prior knowledge in the existing neural networks, the required numbers of training patterns are drastically reduced. Further, the proposed approach is capable for accurately estimating the radiation characteristics in extrapolation region too. The proposed neural approach is also validated with measured results. A very good agreement is achieved in simulated, estimated, and measured results.  2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:673680, 2014.
radiation characteristics different slots microstrip antenna neural networks prior knowledge rectangular patch