Abstract
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.
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Rotshtein, A.P., Rakytyanska, H.B. (2012). Inverse Inference with Fuzzy Relations Tuning. In: Fuzzy Evidence in Identification, Forecasting and Diagnosis. Studies in Fuzziness and Soft Computing, vol 275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25786-5_6
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