Neuro Fuzzy

Neuro-fuzzy systems combine the strengths of neural networks and fuzzy logic to create interpretable and adaptable models for complex data analysis. Current research focuses on improving the scalability and accuracy of these systems, particularly for high-dimensional data, through innovative rule generation techniques (like those employing evolutionary algorithms) and the integration of recurrent or stochastic architectures. These advancements are driving applications in diverse fields, including medical diagnosis (e.g., potassium level prediction from ECGs, COVID-19 detection), industrial data analytics, and reinforcement learning, where interpretability is crucial. The ability to handle high-dimensional data and improve model interpretability are key objectives driving ongoing research.