  The hybrid clustering-classification neural network is proposed. This network
allows increasing a quality of information processing under the condition of
overlapping classes due to the rational choice of a learning rate parameter and
introducing a special procedure of fuzzy reasoning in the clustering process,
which occurs both with an external learning signal (supervised) and without the
one (unsupervised). As similarity measure neighborhood function or membership
one, cosine structures are used, which allow to provide a high flexibility due
to self-learning-learning process and to provide some new useful properties.
Many realized experiments have confirmed the efficiency of proposed hybrid
clustering-classification neural network; also, this network was used for
solving diagnostics task of reactive arthritis.
