Neural networks enhance fuzzy logic by adding the ability to learn from data, optimize fuzzy parameters, and handle complex, non-linear relationships. This combination leads to systems that are more flexible, adaptive, and efficient, particularly in environments where traditional rule-based fuzzy systems might struggle. :)

Farkad Adnan

A neural network enhances fuzzy logic by enabling dynamic learning and adaptation to improve decision-making. Fuzzy logic handles uncertainty by defining rules and linguistic variables but cannot learn from data. Neural networks complement this by optimizing fuzzy rules and membership functions based on data inputs. In my experience, combining these approaches in anomaly detection for smart grid systems improved the accuracy of identifying irregular consumption patterns by training the neural network to fine-tune fuzzy inference parameters. This synergy provides robust and adaptive solutions, especially for complex, non-linear systems.