RESUMO
In this paper the applicability of evolution strategies, a special kind of evolutionary algorithms, to the problem of parameter optimization in the development of fuzzy rule-based systems is demonstrated. For this aim we introduce a shell which supports the design of any kind of rule based systems employing fuzzy logic for the formalization of imprecise reasoning processes and which optimizes all numerical parameters. This method works model-free, we do not need to know implicit features of the optimizing system.
RESUMO
Even today, the diagnosis of acute abdominal pain represents a serious clinical problem. The medical knowledge in this field is characterized by uncertainty, imprecision and vagueness. This situation lends itself especially to be solved by the application of fuzzy logic. A fuzzy logic-based expert system for diagnostic decision support is presented (MEDUSA). The representation and application of uncertain and imprecise knowledge is realized by fuzzy sets and fuzzy relations. The hybrid concept of the system enables the integration of rule-based, heuristic and case-based reasoning on the basis of imprecise information. The central idea of the integration is to use case-based reasoning for the management of special cases, and rule-based reasoning for the representation of normal cases. The heuristic principle is ideally suited for making uncertain, hypothetical inferences on the basis of fuzzy data and fuzzy relations.
