Global asymptotic stability of delayed fractional-order complex-valued fuzzy cellular neural networks with impulsive disturbances

Aravind, R. Vijay; Balasubramaniam, P.

doi:10.1007/s12190-022-01726-x

Cited by 20 publications

(7 citation statements)

References 39 publications

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“…This demonstrates the importance of an event-triggered, observer-based control strategy. Example 2: The fractional-order DC-DC Boost converter [10] is shown in Fig. 12, where C is capacitor and L is inductor are energy components of fractional order.…”

Section: Numerical Examplesmentioning

confidence: 99%

Output feedback control for uncertain fractional-order system subject to deception cyber-attacks via observer-based event-triggered scheme 1

Ali,

Tajudeen,

Rajchakit

et al. 2023

Preprint

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This article investigates the problem of output feedback control for fractional-order (0 1) uncertain systems subject to deception attacks based on observer-based event-triggered scheme. Compared to the existing deception attacks, the proposed model consists of two random deception attacks on the fractional-order system. To decrease communication resource usage while maintaining desired system performance, an observer-based event-triggered scheme has been proposed. But it also has no Zeno behavior. The Lyapunov functional theory is used to establish sufficient conditions for ensuring the global asymptotic stability of an uncertain fractional-order system. By generalized applying singular value decomposition matrix, linear matrix inequalities (LMIs), we obtained the observer, and controller gain matrices. Finally, DC-DC power converter model is used to demonstrate the efficiency of the proposed methodology.

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Section: Numerical Examplesmentioning

confidence: 99%

Output feedback control for uncertain fractional-order system subject to deception cyber-attacks via observer-based event-triggered scheme 1

Ali,

Tajudeen,

Rajchakit

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, in switching the signal of FONNs, an adaptive control has provided better performance of neurons compared to the feedback control. Until now, different types of FONNs models has been deeply studied for the FTS problems such as BAM FONNs [43], memristive FONNs [35], memritive Bam FONNs [29], discontinuous activations-based FONNs [29], fuzzy inertial FONNs [12], fuzzy cellular FONNs [2], reaction-diffusion FONNs [25], inertial FONNs [36], fuzzy inertial Bam FONNs [50], stochastic FONNs [30], and stochastic BAM FONNs [43].…”

Section: Introductionmentioning

confidence: 99%

Finite-time synchronization of fractional order neural networks via sampled data control with time delay

Jose,

Parthiban

2024

J. Math. Computer Sci.

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This paper investigates the problem of finite-time synchronization (FTS) of fractional-order neural networks (FONNs) with time-delay via sampled data control (SDC) scheme. To achieve FTS criteria, a sampled-data control (SDC) scheme is implemented in the slave model of FONNs. And, this investigation is based on the solution of the time-delayed NNs by using Laplace transform, Mittag-Leffler function (MLF), and the generalized Grownwall inequality. Furthermore, under the proposed SDC scheme, the FTS conditions are derived for two cases of fractional order α, such as 0 < α < 1 and 1 < α < 2. The derived conditions ensure that the slave FONNs is asymptotically synchronized with master FONNs. Finally, two numerical examples are given to show the effectiveness of derived FTS criteria, for fractional order lying between 0 < α < 1 and 1 < α < 2.

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“…Furthermore, in [22], Zhang and Xiang investigated the existence, uniqueness and global asymptotic stability of delayed FCNN by using the properties of M-matrix and the topological degree theory. And in [23], Aravind and Balasubramaniam use the fractional Barbalat's lemma and some inequalities to design a new Lyapunov-Krasovskii functional method to discussed the global asymptotic stability of fractional order complex valued FCNN with impulsive interference attentively. At the same time, the available methods and techniques to explore the robustness of stability of FCNN with disturbances are very limited.…”

Section: Introductionmentioning

confidence: 99%

Robustness Analysis of Fuzzy Cellular Neural Network With Deviating Argument and Stochastic Disturbances

Fang

Xie

2023

IEEE Access

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Robustness analysis of fuzzy cellular neural networks with deviating arguments and stochastic disturbances is the main topic of discussion in this paper. The issue at hand is what the upper bounds of the disturbances and deviating intervals for the fuzzy cellular neural network can withstand before losing its stability. We solve these problems by using Gronwall-Bellman lemma and some inequality techniques. The theoretical results point that for an exponentially stable fuzzy cellular neural network, the perturbed fuzzy cellular neural network still keep its globally exponential stability if the upper bound of the length of deviating intervals or the intensity of stochastic disturbances is less than the upper bound derived in this paper. A number of numerical cases are offered to support the availability of conjectural values.INDEX TERMS Fuzzy cellular neural network, robustness analysis, deviating argument, stochastic disturbances.

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Global asymptotic stability of delayed fractional-order complex-valued fuzzy cellular neural networks with impulsive disturbances

Cited by 20 publications

References 39 publications

Output feedback control for uncertain fractional-order system subject to deception cyber-attacks via observer-based event-triggered scheme 1

Output feedback control for uncertain fractional-order system subject to deception cyber-attacks via observer-based event-triggered scheme 1

Finite-time synchronization of fractional order neural networks via sampled data control with time delay

Robustness Analysis of Fuzzy Cellular Neural Network With Deviating Argument and Stochastic Disturbances

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