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Developing New Solutions for a Reconfigurable Microstrip Patch Antenna by Inverse Artificial Neural Networks

This paper presents the use of inverse artificial neural networks (ANNs) to develop and optimize a reconfigurable 5-fingers shaped microstrip patch antenna. New solutions are produced by using three accurate prior knowledge inverse ANNs with sufficient amount of training data where the frequency information is incorporated into the structure of ANNs. The proposed antenna can operate with four modes, which are controlled by two PIN diode switches with ON/OFF states, and it resonates at multiple frequencies between 2-7 GHz. The complexity of the input/output relationship is reduced by using prior knowledge. Three independent methods of incorporating knowledge in the second step of the training process with a multilayer perceptron (MLP) in the first step are demonstrated and their results are compared to EM simulation.