1. An Evolving Neuro-Fuzzy System with Online Learning/Self-learning
- Author
-
Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., and Deineko, Anastasiia O.
- Subjects
Computer Science::Machine Learning ,FOS: Computer and information sciences ,ComputingMethodologies_PATTERNRECOGNITION ,Artificial Intelligence (cs.AI) ,Quantitative Biology::Neurons and Cognition ,Computer Science - Artificial Intelligence ,Computer Science - Neural and Evolutionary Computing ,Neural and Evolutionary Computing (cs.NE) - Abstract
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.
- Published
- 2016
- Full Text
- View/download PDF