This paper deals with the restoration and the identification of the causes (diagnoses) through the observed effects (symptoms) on the basis of fuzzy relations and Zadeh's compositional rule of inference. We propose an approach for building fuzzy systems of diagnosis, which enables solving fuzzy relational equations together with design and tuning of fuzzy relations on the basis of expert and experimental information. The essence of tuning consists of the selection such membership functions for fuzzy causes and effects, and also fuzzy relations, which minimize the difference between model and experimental results of diagnosis. The genetic algorithm is used for solving the optimization problem. The proposed approach is illustrated by the computer experiment and the real example of diagnosis.Index Terms-Diagnostic expert systems, fuzzy systems, inverse problems, training.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.