General type-2 fuzzy multi-switching synchronization of fractional-order chaotic systems

Sabzalian, Mohammad Hosein; Mohammadzadeh, Ardashir; Zhang, W.; Jermsittiparsert, Kittisak

doi:10.1016/j.engappai.2021.104163

Cited by 13 publications

(4 citation statements)

References 32 publications

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“…In the first examination, the stabilization performance is investigated and it shows that all states are perfectly converged to zero at a finite time. For the second examination, the suggested controller is applied for a synchronization problem and it is shown that the case study financial chaotic system well tracks a nonidentical chaotic system; the synchronization errors have [46] 0.8421 1.0871 1.2820 T2-FLC [47] 0.4023 0.9142 1.0621 GT2-FLC [48] 0.4128 0.8071 1.0278 reached zero. For the last examination, the accuracy is evaluated under a high-level noisy condition and it is shown that the suggested control scenario gives perfect results in the presence of high dynamic perturbations and unknown dynamics.…”

Section: Discussionmentioning

confidence: 99%

“…To examine the superiority of the suggested control scenario versus other similar approaches, a comparison is presented. e root-mean-square of synchronization errors (RMSEs) for the designed controller are compared with those the type-1 FLS-based controller (T1-FLC) [46], type-2 FLS-based controller (T2-FLC) [47], and generalized FLSbased controller (GT2-FLC) [48]. e comparison results are shown in Table 2.…”

Section: Complexitymentioning

confidence: 99%

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

et al. 2022

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In this paper, a new approach is presented for stabilizing and synchronizing financial chaotic systems. A new type-3 (T3) fuzzy-based system (FLS) with an online optimization scheme is designed to cope with chaotic behavior, high-level uncertainties, and unknown dynamics. An adaptive compensator also eliminates the effect of approximation errors (AEs) and perturbations. The stability of the dynamics of synchronization errors is guaranteed by the use of the Lyapunov method. Several simulations and comparisons demonstrate the superiority of the suggested control and synchronization scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Complexitymentioning

confidence: 99%

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

et al. 2022

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“…Therefore, TIC has some problems with monitoring failure in practical applications: there are false alarms and under report. These monitoring failures, which are not conducive to the field staff to accurately understand the process operation status, will affect the operator's decisionmaking, and may even lead to serious production accidents [7]. Therefore, in order to achieve better monitoring of the actual industrial process, the kernel learning method, which can effectively improve the uncertainty, strong coupling, and nonlinearity of the data, is introduced into TIC.…”

Section: Introductionmentioning

confidence: 99%

Microbial fermentation optimal control method based on improved particle swarm optimization

Liang

2023

Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022)

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Microbial fermentation is a typical microbial fermentation process. Microbial bacteria ingest the nutrients of raw materials in the fermentation tank. Under appropriate conditions, enzymes in the body catalyze complex biochemical reactions to produce microorganisms. In order to guarantee the quality of modeling data and meet the accuracy, integrity, and consistency of data quality requirements, it needs to preprocess the input and output data. In this paper, the parameter model is solved by the particle swarm algorithm. Updating the parameter value of the next moment in real time constitutes a feedback correction to the prediction model. Theil inequality approach is adopted to test the tracking performance of the above model’s adaptive correction method. The Monte Carlo method is applied to generate multiple groups of different kinetic model values, which are substituted into the fermentation kinetic model as the real model parameter values. After the experimental analysis, the measured value of the model established by the method in this paper is closer to the predicted value, which has the effect of feedback correction and optimal control. The external conditions in the fermentation process are optimally controlled to achieve the effects of shortening the production period. It improves the yield of fermentation terminal target products and reduces the consumption of raw materials.

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“…For instance, in [17], a type-2 (T2) FLS with a tuned scheme for secondary memberships has been implemented for the frequency regulation problem subject to unknown dynamics and multiple disturbances. In [18], based on T2-FLSs, the unknown nonlinearities have been approximated to analyze the synchronization of chaotic systems under unknown slave systems. The problem of synchronization of chaotic systems in the presence of unknown dynamics has been solved by using a dynamic programming technique and type-2 wavelet-based FLSs [19].…”

Section: Introductionmentioning

confidence: 99%

A New Data-Driven Control System for MEMSs Gyroscopes: Dynamics Estimation by Type-3 Fuzzy Systems

Alattas

Mohammadzadeh²,

Mobayen

et al. 2021

Micromachines

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In this study, a novel data-driven control scheme is presented for MEMS gyroscopes (MEMS-Gs). The uncertainties are tackled by suggested type-3 fuzzy system with non-singleton fuzzification (NT3FS). Besides the dynamics uncertainties, the suggested NT3FS can also handle the input measurement errors. The rules of NT3FS are online tuned to better compensate the disturbances. By the input-output data set a data-driven scheme is designed, and a new LMI set is presented to ensure the stability. By several simulations and comparisons the superiority of the introduced control scheme is demonstrated.

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General type-2 fuzzy multi-switching synchronization of fractional-order chaotic systems

Cited by 13 publications

References 32 publications

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

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

Microbial fermentation optimal control method based on improved particle swarm optimization

A New Data-Driven Control System for MEMSs Gyroscopes: Dynamics Estimation by Type-3 Fuzzy Systems

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