🤖 AI Summary
To address slow convergence, severe oscillations, and learning instability in fuzzy system modeling—caused by rule discontinuities and parameter uncertainty—this paper proposes an end-to-end differentiable fuzzy modeling and self-learning framework based on smooth composition operators. The core innovation is the first introduction of a smooth T-norm and S-implication composite structure, enabling global differentiability of both membership functions and the inference process. This facilitates gradient-driven adaptive updating of fuzzy rules and joint optimization of antecedent and consequent parameters. Evaluated on multiple nonlinear system identification benchmarks, the method achieves substantial improvements: enhanced modeling accuracy and generalization capability, 40% faster convergence, and a 75% reduction in rule oscillation—without incurring significant computational overhead.