<ul><li>Uncertainty Quantification (UQ) is crucial for deploying reliable Deep Learning (DL) models in high-stakes applications.</li><li>A blueprint calibration strategy for General Type-2 Fuzzy Logic Systems (GT2-FLSs) is proposed to improve efficiency and adaptability for generating Prediction Intervals (PIs) in new coverage levels.</li><li>Two calibration methods, a lookup table-based approach and a derivative-free optimization algorithm, are developed to achieve accurate and reliable PIs while reducing computational overhead.</li><li>Experimental results demonstrate the superior performance of the calibrated GT2-FLS in Uncertainty Quantification, emphasizing its potential for practical applications.</li></ul>

Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration Strategy

Discover more