Abstract
The identification of an object consists of the construction of its mathematical model, i.e., an operator of connection between input and output variables from experimental data. Modern identification theory [1 – 3], based on modeling dynamical objects by equations (differential, difference, etc.), is poorly suited for the use of information about an object in the form of expert IF-THEN statements. Such statements are concentrated expertise and play an important role in the process of human solution of various cybernetic problems: control of technological processes, pattern recognition, diagnostics, forecast, etc. The formal apparatus for processing expert information in a natural language is fuzzy set theory [4, 5]. According to this theory, a model of an object is given in the form of a fuzzy knowledge base, which is a set of IF-THEN rules that connect linguistic estimates for input and output object variables. The adequacy of the model is determined by the quality of the membership functions, by means of which linguistic estimates are transformed into a quantitative form. Since membership functions are determined by expert methods [5], the adequacy of the fuzzy model depends on the expert qualification.
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Rotshtein, A.P., Rakytyanska, H.B. (2012). Fuzzy Rules Tuning for Direct Inference. 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_3
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DOI: https://doi.org/10.1007/978-3-642-25786-5_3
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