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Adaptive Fuzzy Predictive Approach in Control

Author

Listed:
  • Anton A. Romanov

    (Department of Information Systems, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Aleksey A. Filippov

    (Department of Information Systems, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Nadezhda G. Yarushkina

    (Department of Information Systems, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

Abstract

This article studies the approach to solving the problem of controlling the complex organizational and technical systems based on hybrid models. We propose a new component of intelligent decision support that is integrated with control systems. The proposed component is based on fuzzy logic and knowledge engineering. We present a model of ontology to form the context of data analysis and time series modeling. The ontological context allows us to represent trends of the analyzed object indicators. An expert can add a set of fuzzy rules to the ontology for systems control based on the fuzzy inference. The proposed approach allows reducing the time of analysis and interpretation of the results. Experimental results confirm the correctness and effectiveness of the approach proposed in this article.

Suggested Citation

  • Anton A. Romanov & Aleksey A. Filippov & Nadezhda G. Yarushkina, 2023. "Adaptive Fuzzy Predictive Approach in Control," Mathematics, MDPI, vol. 11(4), pages 1-18, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:875-:d:1062458
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    References listed on IDEAS

    as
    1. Vilém Novák & Soheyla Mirshahi, 2021. "On the Similarity and Dependence of Time Series," Mathematics, MDPI, vol. 9(5), pages 1-14, March.
    2. Anton Romanov & Julia Stroeva & Aleksey Filippov & Nadezhda Yarushkina, 2021. "An Approach to Building Decision Support Systems Based on an Ontology Service," Mathematics, MDPI, vol. 9(22), pages 1-23, November.
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