A hybrid global learning algorithm based on global search and least squares techniques for backpropagation networks

A hybrid learning algorithm for backpropagation network based on global search and least squares methods is presented to increase the speed of convergence. The proposed algorithm comprises global search and least squares parts. The global search part trains a backpropagation network over a reduced weight space. The remained weights are calculated in accordance with linear least squares method. Two problems of nonlinear function approximation and modified XOR are applied to demonstrate the fast global search performance of the proposed algorithm. The results indicate that the proposed algorithm enables the learning process to significantly speed up by at most 4670% in terms of iterations and do not trap in local minima.