A Comparison of PSO and Backpropagation for Training RBF Neural Networks for Identification of a Power System with STATCOM

Salman Mohagheghi
Ganesh K. Venayagamoorthy, Missouri University of Science and Technology
Yamille del Valle
Ronald G. Harley

This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/772

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Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method.