Despite all the progress in neural networks field the technology is
brittle and sometimes difficult to apply. Good initialization of adaptive
parameters in neural networks and optimization of architecture are the key
factor to create robust neural networks. Methods of initialization of MLPs
are reviewed and new methods based on clusterization techniques are
suggested. Penalty term added to the error function leads to optimized,
small and accurate networks.
