Soft computing exploits the biological processes to simplify scientific and technical problems. Correspondingly, soft computing is employed in Frequency Selective Surface designing. In this particular endeavor a Back Propagation Algorithm trained Artificial Neural Network is reported for the designing of single layer Frequency Selective Surface. The prime aspiration was to ascertain the Resonant Frequency and the Band Width of a crossed dipole Frequency Selective Surface. In due course of action and to attain maximized throughput latterly a hybrid Particle Swarm Optimization trained Artificial Neural Network Algorithm is formulated. The empirical study confirmed that Hybrid Particle Swarm Optimization trained Artificial Neural Network is amply efficient and effective for global and fast local searching procedures. Afterward a comparative analysis of Hybrid Particle Swarm Optimization and Back Propagation Algorithm is contemplated.

ANN, FSS, BPA, HPNNA, PSO

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