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.
@article{arxiv.1610.06488,
title = {An Evolving Neuro-Fuzzy System with Online Learning/Self-learning},
author = {Yevgeniy V. Bodyanskiy and Oleksii K. Tyshchenko and Anastasiia O. Deineko},
journal= {arXiv preprint arXiv:1610.06488},
year = {2016}
}