Aperture antenna shape prediction by feedforward neural networks

The emergence of adaptive "smart" materials has led to the design of active aperture antennas. Inherent in these antennas is the ability to change their shape in real time to meet various performance characteristics. When examining the usefulness of these antennas, one of the primary concerns is the antenna shape needed for a particular radiation pattern. Aperture antenna shape prediction is also a concern in the industrial production of semi-paraboloidal antennas. The work in this study employs an artificial neural network to model the aperture antenna shape in real time. To test the accuracy of the network, the "threefold holdout technique" was employed. In this technique, sets of examples are "held out" of the training process and used to obtain the "true error" of the network. The network accurately predicted the aperture shape exactly, to within three significant digits, 96% of the time.