Reduction of adjusting weights space dimension in feedforward artificial neural networks training

This report provides an approach to the reduction of the adjusting weights space dimension in two-layer multioutput feedforward artificial neural networks training. Our approach is based on linear-nonlinear network structure with respect to weights. Two training algorithms based on the Newton and Gauss method with pseudo-inversion for optimization were deduced. Training algorithms are extended to multilayer networks. The report carries the information about the analysis of the proposed training algorithms. Results of numerical experiments are also included.