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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Inverse Inference with Fuzzy Relations Tuning
verfasst von : Alexander P. Rotshtein, Hanna B. Rakytyanska
Erschienen in: Fuzzy Evidence in Identification, Forecasting and Diagnosis
Verlag: Springer Berlin Heidelberg
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Diagnosis, i.e. determination of the identity of the observed phenomena, is the most important stage of decision making in different domains of human activity: medicine, engineering, economics, military affairs, and others. In the case of the diagnosis of problems where physical mechanisms are not well known due to high complexity and nonlinearity, a fuzzy relational model may be useful. A fuzzy relational model for simulating cause and effect connections in diagnosing problems has been introduced by Sanchez [1, 2]. A model for diagnosis can be built on the basis of Zadeh’s compositional rule of inference [3], in which the fuzzy matrix of “causes-effects” relations serves as the support of the diagnostic information. In this case, the problem of diagnosis amounts to solving fuzzy relational equations.