Type 2 Fuzzy

Type-2 fuzzy logic systems extend traditional fuzzy logic by incorporating uncertainty in the membership functions themselves, enabling more robust handling of imprecise or ambiguous data. Current research focuses on enhancing learning algorithms for these systems, particularly addressing challenges in high-dimensional data and generating reliable prediction intervals, with adaptations of existing methods like Takagi-Sugeno-Kang models and the integration of deep learning techniques. These advancements are proving valuable in diverse applications, including image denoising, improving the accuracy of physiological signal processing (e.g., PPG analysis), and developing more explainable AI models for time-dependent processes and financial market prediction.