An Evolving Neuro-Fuzzy System with Online Learning/Self-learning

TL;DR

This paper introduces an evolving neuro-fuzzy system capable of online and self-learning, which adaptively tunes weights and membership functions to handle uncertain data environments effectively.

Contribution

It presents a novel neuro-fuzzy architecture that combines supervised and self-learning paradigms for online adaptation under uncertainty.

Findings

Proven effectiveness of the architecture and learning procedure

System adapts in real-time to data changes

Handles uncertainty in data processing

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.