An Evolving Neuro-Fuzzy System with Online Learning/Self-learning
This work addresses data processing under uncertainty for applications requiring adaptive systems, but it appears incremental as it builds on existing neuro-fuzzy methods.
The authors tackled the problem of processing uncertain data by proposing a new neuro-fuzzy system architecture that tunes synaptic weights and membership functions using supervised and self-learning paradigms, with results demonstrating its effectiveness.
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.