Fuzzy neural network methodology applied to medical diagnosis

This paper presents a technique for building expert systems that combines the fuzzy-set approach with artificial neural network structures. This technique can effectively deal with two types of medical knowledge: a nonfuzzy one and a fuzzy one which usually contributes to the process of medical diagnosis. Nonfuzzy numerical data is obtained from medical tests. Fuzzy linguistic rules describing the diagnosis process are provided by a human expert. The proposed method has been successfully applied in veterinary medicine as a support system in the diagnosis of canine liver diseases

Fuzzy inequational logic

We present a logic for reasoning about graded inequalities which generalizes the ordinary inequational logic used in universal algebra. The logic deals with atomic predicate formulas of the form of inequalities between terms and formalizes their semantic entailment and provability in graded setting which allows to draw partially true conclusions from partially true assumptions. We follow the Pavelka approach and define general degrees of semantic entailment and provability using complete residuated lattices as structures of truth degrees. We prove the logic is Pavelka-style complete. Furthermore, we present a logic for reasoning about graded if-then rules which is obtained as particular case of the general result

Tropical linear algebra with the Lukasiewicz T-norm

The max-Lukasiewicz semiring is defined as the unit interval [0,1] equipped with the arithmetics "a+b"=max(a,b) and "ab"=max(0,a+b-1). Linear algebra over this semiring can be developed in the usual way. We observe that any problem of the max-Lukasiewicz linear algebra can be equivalently formulated as a problem of the tropical (max-plus) linear algebra. Based on this equivalence, we develop a theory of the matrix powers and the eigenproblem over the max-Lukasiewicz semiring.Comment: 27 page