Modeling the Context of the Problem Domain of Time Series with Type-2 Fuzzy Sets

Romanov, Anton; Filippov, A. A.; Voronina, Valeria V; Guskov, Gleb; Yarushkina, Nadezhda

doi:10.3390/math9222947

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…The development of this approach will be integrated with the previously presented approach to contextually analyze the dynamics of the control system indicators [25]. We provided a comparison of the manual analysis and proposed an approach in terms of time spent on analysis operations.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Time Series Forecasting during Software Project State Analysis

Romanov,

Yarushkina,

Filippov

et al. 2023

Mathematics

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Repositories of source code and their hosting platforms are important data sources for software project development and management processes. These sources allow for the extraction of historical data points for the product development process evaluation. Extracted data points reflect the previous development experience and allow future planning and active development tracking. The aim of this research is to create a predictive approach to control software development based on a time series extracted from repositories and hosting platforms. This article describes the method of extracting parameters from repositories, the approach to creating time series models and forecasting their behavior. Also, the article represents the proposed approach for software project analyses based on fuzzy logic principles. The novelty of this approach is the ability to perform an expert evaluation of different stages of software product development based on the forecasted values of interested parameters and a fuzzy rule base.

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Section: Experimental Setup and Resultsmentioning

confidence: 99%

Time Series Forecasting during Software Project State Analysis

Romanov,

Yarushkina,

Filippov

et al. 2023

Mathematics

Self Cite

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“…The interval type-2 FLSs (IT2-FLSs) outperformed in comparison with the type-I counterparts [22,23]. In a few studies, IT2-FLSs have been used in glucose regulation problems.…”

Section: Introductionmentioning

confidence: 99%

Generalized Type-2 Fuzzy Control for Type-I Diabetes: Analytical Robust System

et al. 2022

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The insulin injection rate in type-I diabetic patients is a complex control problem. The mathematical dynamics for the insulin/glucose metabolism can be different for various patients who undertake different activities, have different lifestyles, and have other illnesses. In this study, a robust regulation system on the basis of generalized type-2 (GT2) fuzzy-logic systems (FLSs) is designed for the regulation of the blood glucose level. Unlike previous studies, the dynamics of glucose–insulin are unknown under high levels of uncertainty. The insulin-glucose metabolism has been identified online by GT2-FLSs, considering the stability criteria. The learning scheme was designed based on the Lyapunov approach. In other words, the GT2-FLSs are learned using adaptation rules that are concluded from the stability theorem. The effect of the dynamic estimation error and other perturbations, such as patient activeness, were eliminated through the designed adaptive fuzzy compensator. The adaptation laws for control parameters, GT2-FLS rule parameters, and the designed compensator were obtained by using the Lyapunov stability theorem. The feasibility and accuracy of the designed control scheme was examined on a modified Bergman model of some patients under different conditions. The simulation results confirm that the suggested controller has excellent performance under various conditions.

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