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This paper compares two optimization methods for training Neural Networks: the typical supervised feed-forward hackpropagation algorithm and an improved Particle Swarm Optimization method. The aim is to highlight advantages and drawbacks of these techniques in order to suitably apply them to electromagnetic problems. Some numerical results and comparisons are presented analyzing a load forecasting problem. Neural Networks are trained for a particular power system load consuption signal, for future time prediction.