Fuzzy Logic

Fuzzy logic models vague or uncertain information using graded membership rather than only true-or-false categories, making it useful when boundaries are imprecise or knowledge is expressed in language. Common approaches include Mamdani and Takagi–Sugeno inference, interval Type-2 fuzzy sets, fuzzy cognitive maps, and neuro-fuzzy systems such as ANFIS; these support decision-making, control, forecasting, and classification. Current work also combines fuzzy rules with neural networks and transformers to add uncertainty handling or interpretable reasoning to learned models. A central challenge is balancing readable, compact rules and well-calibrated uncertainty with predictive accuracy, scalability, and reliable evaluation across changing settings.