Neuro-Fuzzy Systems: A Study of Architectures, Applications, and Future Directions

Abstract

ANNs and Fuzzy Systems are two of the most popular computational models that are each characterized by their own advantages. ANN’s strength lies in modification and capability to learn from the inputs; whereas Fuzzy Systems are good at giving interpretability and are proficient to handle uncertainty. The incorporation of these paradigms into Neuro-Fuzzy Systems just harnesses the strength of these systems providing optimal solutions for complicated, volatile, and uncertain problems. This survey seeks to discuss and analyse the basic concepts of ANNs and Fuzzy Systems with an emphasis on the architectures of the two, the learning methodologies associated with them and the advantages and disadvantages of the ANNs and Fuzzy Systems. It focuses on the interaction that results from integration and shows how the Neuro-Fuzzy Systems gain in terms of flexibility, interpretability and robustness. The most recent developments, including hybrid learning algorithm, optimization, and lightweight architecture, are presented, and the use of the techniques is demonstrated in various fields including robotics, data analysis, diagnosis, the Internet of Things, and autonomous systems. The paper also covers limitations like computational costs, scalability and some of the ethical issues, and areas of further research like quantum computing, real time processing and novel use of LSTMs. Thus, the present survey underlines how Neuro-Fuzzy Systems are capable to solve many real-world issues and how they are the key tools for the further development of the Artificial Intelligence.