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A novel synthesis artificial neural network (SYNTHESIS-ANN) is combined with the finite-difference time-domain method. Practical applications are illustrated through the optimization of a dipole antenna input impedance. The ANN architecture utilizes a hetero-associative memory, which exploits a fault tolerant number representation of a neural network for input and output data. In addition, the number representation reveals significant insight into a new method of fault tolerant computing. A new randomization process for the synthesis of antenna geometrical parameters is presented. Additional work is required to investigate the potential of this new paradigm.