A new approach to inference in approximate reasoning
Fuzzy Sets and Systems
Propagation of uncertainty and imprecision in knowledge-based systems
Fuzzy Sets and Systems
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
Trapezoidal approximations of fuzzy numbers---revisited
Fuzzy Sets and Systems
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
Generalized modus Ponens using various implications and T-norm product with threshold
Annals of Mathematics and Artificial Intelligence
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Using Generalized Modus Ponens reasoning, we examine the values of inferred conclusion when the premise of the rule and the observed fact have a partial overlapping. We work with fuzzy if-then rules with a single input single output and the t-norm t(x, y) = max((1+λ)(x+y-1)-λxy,0), λ ≥ -1, for composition operation. This t-norm is important to use because for λ = -1 and λ = 0 it gives the very used t-norms t1(x, y) = xy and t2(x, y) = max(0, x+y-1), respectively. A set of eight implication operators are used, completing some of our previous results, where the method were used analyzing the possible inclusion relations between the premise and the observation.