Fuzzy associative search procedure for process identification

Bakhtadze, Natalia; Kulba, Vladimir; Lototsky, Vladimir A.; Maximov, Evgeny M.; Pavlov, B. V.

doi:10.3182/20100701-2-pt-4011.00052

Cited by 2 publications

(4 citation statements)

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“…Associative search method consists in constructing virtual predictive models. The term "virtual" should be understood as "ad hoc" [2]. The method presumes the construction of a predictive model for a dynamic object as follows.…”

Section: Associative Search Techniquementioning

confidence: 99%

“…The use of additional a priori information on the system for its training is considered by some authors today to be one of the key trends in the theory and practice of identification [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…One method that implements this approach to identification is the associative search method based on the design of predictive models [2]. They are based on inductive learning, that is, on associative search of analogs by means of intelligent analysis of process history and knowledge base development.…”

Section: Introductionmentioning

confidence: 99%

“…The development of intelligent identification algorithms for nonlinear and nonstationary objects is important for various applications, in particular, in chemical, oil refining, and power (smart grids) industries; transportation and logistics system; and trading processes (Bakhtadze et al [1,2,[4][5][6][7]).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Data Mining-Based Identification of Nonlinear Systems

Bakhtadze¹,

Lototsky²,

Pyatetsky³

et al. 2019

Applied Modern Control

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This chapter presents identification methods using associative search of analogs and wavelet analysis. It investigates the properties of data mining-based identification algorithms which allow to predict: (i) the approach of process variables to critical values and (ii) process transition to chaotic dynamics. The methods proposed are based on the modeling of human operator decision-making. The effectiveness of the methods is illustrated with an example of product quality prediction in oil refining. The development of fuzzy analogs of associative identification models is further discussed. Fuzzy approach expands the application area of associative techniques. Finally, state prediction techniques for manufacturing resources are developed on the basis of binary models and a machine learning procedure, which is named associative rules search.

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Section: Associative Search Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%