Frequency selective surface design based on iterative inversion of neural networks

This paper proposes a novel approach to solve a constrained inverse problem encountered in the design of frequency selective surfaces (FSS's). Due to the many-to-one nonlinear functional relationship between an FSS and its frequency response, there is no closed form solution directly from the given desired frequency response to the corresponding surface. Therefore, to design an FSS for a given response, one has to search in the knowledge base through a trial-and-error procedure. This procedure can be a very laborious and tedious process. Our approach adopts an iterative regularized inversion technique, which starts with an inversion algorithm for multilayer perceptrons to generate the corresponding 2-D surface for the given desired frequency response, a constraint satisfaction mechanism is then used to reshape the 2-D surface to satisfy the constraints, and the resulting surface is used as the initial point for the next inversion algorithm. This procedure is mathematically similar to the projection onto convex set algorithm for constrained optimization problems. 1 Frequency Selective Surface Design Frequency selective surfaces (FSS7s) have widespread applications over much of the electromagneti