A Type-3 Fuzzy Approach for Stabilization and Synchronization of Chaotic Systems: Applicable for Financial and Physical Chaotic Systems

Tian, Man-Wen; Bouteraa, Yassine; Alattas, Khalid A.; Yan, Shu‐Rong; Alanazi, Abdullah K.; Mohammadzadeh, Ardashir; Mobayen, Saleh

doi:10.1155/2022/8437910

Cited by 9 publications

(1 citation statement)

References 48 publications

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“…Among them, we can mention its applications in medical issues, financial issues, encryption techniques, control systems and optimization algorithms. Tian et al [ 40 ] introduced a new deep-learned type-3 fuzzy logic system to develop a model for CO2 solubility estimation. Moreover, a new type-3 fuzzy logic system with an online optimization scheme is proposed for stabilizing and synchronizing financial chaotic systems [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

Stability analysis with general fuzzy measure: An application to social security organizations

Arabjazi

Rostamy-Malkhalifeh²,

Lotfı³

et al. 2022

PLoS ONE

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An effective method for evaluating the efficiency of peer decision-making units (DMUs) is data envelope analysis (DEA). In engineering sciences and real-world management problems, uncertainty in input and output data always exists. To achieve reliable results, uncertainties must be taken into account. In this research, a General Fuzzy (GF) approach is designed to cope with uncertainty in the presence of fuzzy observations for categorizing and specifying stability radius and alterations ranges of efficient and inefficient DMUs, which is applicable to real-world decision-making problems. For this purpose, a DEA sensitivity analysis model is presented, which will be modeled by fuzzy sets. Then, by applying the General Fuzzy (GF) approach, the fuzzy DEA sensitivity analysis model is transformed into the equivalent crisp form of fuzzy chance constraints according to specific confidence levels. Finally, a numerical example and a case study of branches of the social security organization are presented to illustrate sensitivity and stability analysis in the presence of fuzzy data. The obtained results provide the input and output changes of the evaluated units according to the attitude and preference of the decision maker with different confidence levels so that the data changes in the fuzzy environment do not change the units’ classification from efficient to inefficient and vice versa.

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Section: Introductionmentioning

confidence: 99%