A novel approach to guarantee good robustness of fuzzy reasoning
Lipschitz aggregation property and copula characteristic of t-norms and implications are discussed and the robustness of rule-based fuzzy reasoning is investigated. According to Lipschitz aggregation property and copula characteristic of t-norms and implications, suitable t-norm and implication can be chosen to satisfy the need of robustness of fuzzy reasoning. In 1-Lipschitz operators, if both t-norm and implication are copulas, the rule-based fuzzy reasoning is much more stable and more reliable. In copulas, if both t-norm and implication are 1-l?-Lipschitz, they can guarantee good robustness of fuzzy reasoning. The approach in the paper provides guidance for choosing suitable fuzzy connective operators and decision making application in rule-based fuzzy reasoning. The experiments not only illustrate the ideas proposed in the paper but also are the applications of intelligent information processing, especially face association.
robustness fuzzy reasoning copula implication perturbation triangular norm lipschitz property