A self-organizing auto-associative network for the generalized physical design of microstrip patches

The current work deals with the efficient physical design of patch antennas given the desired parameters like resonant frequency f/sub r/, feed point position a/sub f/, substrate thickness h, relative permittivity (epsilon)/sub r/, input impedance Z (=R+jX), and efficiency (eta). Based loosely on the analogy of perception of the human brain, a neurocomputing network has been designed, consisting of two distinct phases, namely, the training phase and the application phase. The training phase accepts as input the exhaustive set of the said parameters for patches of different shapes and sizes and determines the optimized processors (processors that adequately define the information topology of the input data set) from the exhaustive training instances using a set of information extracting self-organizing neural networks. The outputs of the training phase are n sets of processors, n being the number of different shapes of patches taken into consideration. The application phase determines the shape and size of a microstrip antenna when its desired parameters are presented to the network as the external input. This is achieved by comparing the external input with each set of processors, hence determining the cost due to each comparison. A cost matrix is thus formed which when passed through an optimization network gives the best match and hence the shape and shape determining attributes of the patch whose parameters had been passed as external input.