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Comparison of two neural network optimization approaches

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4 Author(s)
Grimaldi, E.A. ; Politecnico di Milano, Dipartimento di Elettrotecnica, Piazza Leonard0 da Vinci 32,20133, Milano, Italy ; Grimaccia, F. ; Mussetta, M. ; Zich, R.E.

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.

Published in:

Mathematical Methods in Electromagnetic Theory, 2004. 10th International Conference on

Date of Conference:

14-17 Sept. 2004