An Evolving Neuro-Fuzzy System with Online Learning/Self-learning
  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. 
Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko and Anastasiia O.   Deineko
cs.AI cs.NE