  An architecture of a new neuro-fuzzy system is proposed. The basic idea of
this approach is to tune both synaptic weights and membership functions with
the help of the supervised learning and self-learning paradigms. The approach
to solving the problem has to do with evolving online neuro-fuzzy systems that
can process data under uncertainty conditions. The results prove the
effectiveness of the developed architecture and the learning procedure.
