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In this paper, we focus on parametric interval-valued full implication algorithms. The interval-valued full implication algorithms based on a class of interval-valued t-norms with parameters are proposed. Meanwhile, the [ a , b ] -type interval-valued full implication algorithms based on a class of interval-valued t-norms with parameters are introduced. Furthermore, the solutions of these algorithms are given. Finally, the proposed algorithms are applied to pattern recognition. [ABSTRACT FROM AUTHOR]

Based on the normalized Minkowski distance in Hausdorff metrics, we study the sensitivity of interval-valued Schweizer-Sklar t-norms and their corresponding residual implications. Moreover, we investigate the robustness of interval-valued fuzzy reasoning triple I algorithms based on Schweizer–Sklar operators and illustrate the feasibility of the algorithms by a numerical example. Finally, the interval-valued fuzzy reasoning triple I algorithms are applied to medical diagnosis. [ABSTRACT FROM AUTHOR]