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Neural networks are systems that can be trained to remember the behavior of a modeled structure at given operational points, and that can be used to approximate the behavior of the structure outside of the training points. These neural-net approximation abilities are demonstrated in the modeling a frequency-selective surface, a microstrip transmission line, and a microstrip dipole. Attention is given to the accuracy and to the efficiency of neural models. The association between neural models and genetic algorithms, which can provide a global design tool, is discussed. Portions of the MATLAB code illustrate the descriptions.