Frequency selective surface design based on iterative inversion of neural networks

A novel approach is presented to solve a constrained inverse problem encountered in the design of frequency selective surfaces (FSSs). 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 laborious and tedious trial-and-error procedure. The authors' 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.