A. V. Yeromin, V. P. Dobritsa
This work describes an approach to the learning of multilayer neural networks, based on the iterative minimization of the network output mistake. This mistake represents difference between calculated and desired vectors. The mistake is divided into two parts: scalar of vector mistake and vector direction mistake. The target of the algorithm is maximum approach of output vector to desired one by the minimization of described mistakes.