Another hybrid algorithm for finding a global mimimum of MLP error functions

This report presents P scg , a new global optimization method for training multilayered perceptrons. Instead of local minima, global minima of the error function are found. This new method is hybrid in the sense that it combines three very different optimization techniques: Random Line Search, Scaled Conjugate Gradient and a 1-dimensional minimization algorithm named P . The best points of each component are retained by the hybrid method: simplicity of Random Line Search, efficiency of Scaled Conjugate Gradient, efficiency and convergence toward a global minimum for P . P scg is empirically shown to perform better or much better than three other global random optimization methods and a global deterministic optimization method. The aim of this research is to provide easy-to-use learning methods for several research projects; in particular these methods will be employed by knowledge-based systems. Changes from previous version of January 19, 1996 : in sections 7.4.2 and 7.4..