Abstract
The problem of optimal identification and processing of random time series (RTS) based on the use of the property of statistical, dynamic, fuzzy models is formulated. A method for the qualitative identification of RTS is proposed, including algorithms for fuzzy equations, logical conclusions, taking into account the effects of environmental factors and the nonstationarity of processes. A generalized algorithm for identifying the RTS with regulation and correction of variables values based on fuzzy logic rules, ways of searching for extrema by norms and -norms is developed. Designed tools for optimal data processing by determining the appropriate model; parametric and structural identification of objects; search optimization; learning models; identification of the dependence of "inputs and outputs"; formation and use of knowledge base, as well as sets of fuzzy rules, linguistic variables, membership functions and algorithms for regulating the values of variables. Methods of fuzzy correction of distorted information by means of the error control of RTS identification have been developed, and a software package has been implemented to providing high accuracy of data processing with significantly lower costs.
First Page
78
Last Page
84
References
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Recommended Citation
Jumanov, I.I and Bekmurodov, Z.T
(2018)
"Methods of optimizing data processing based on fuzzy correction of time series elements and variable identification models,"
Chemical Technology, Control and Management: Vol. 2018:
Iss.
2, Article 15.
DOI: https://doi.org/10.34920/2018.3.78-84
