Global exponential stability analysis of anti-periodic of discontinuous BAM neural networks with time-varying delays

Radhakrishnan, N.; Kodeeswaran, R.; Raja, R.; Maharajan, C.; Stephen, A Rizzi

doi:10.1088/1742-6596/1850/1/012098

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Numerous coupled dynamic systems are modelable as chaotic nonlinear systems in several contexts, including physics, nature, and society [22,25,26]. Therefore, there is significant research value in studying chaotic systems and their synchronization.…”

Section: Introductionmentioning

confidence: 99%

Robust impulse nonlinear delayed multi-agent systems: an exponential synchronization

Kaewbanjak,

Stephen,

Srinidhi

et al. 2024

J. Math. Computer Sci.

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This work explores the use of state-feedback pinning control in the presence of time-varying delay to solve the synchronization problem of uncertain nonlinear multi-agent systems (MASs). To begin, it is assumed that the agent's communication topology is a directed, static network. Second, the synchronization issue is transformed into the typical closed-loop system stability issue by employing Laplacian matrix inequality (LMI). The primary goal of this study is to construct a state-feedback pinning controller that yields a closed-loop system that is stable under all permissible uncertainty and impulsive cases. To achieve this goal, we develop a new set of delay-dependent synchronization criteria for the closed-loop system by constructing an appropriate Lyapunov functional and making use of Kronecker product features in conjunction with matrix inequality approaches. All that's needed to construct the optimal state-feedback controller is a set of constraints in the form of linear matrix inequalities, which can be solved with any number of powerful optimization methods. To further illustrate the practicality and efficiency of the suggested control design system, a numerical example and associated simulations are provided.

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Section: Introductionmentioning

confidence: 99%

Robust impulse nonlinear delayed multi-agent systems: an exponential synchronization

Kaewbanjak,

Stephen,

Srinidhi

et al. 2024

J. Math. Computer Sci.

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“…Exponential passivity seamlessly integrates stability with a system's energy behavior, which is crucial in maintaining overall stability and energy efficiency. Recent works have addressed the study of exponential passivity for NNs with time-varying delay [28,46,47,52]. Furthermore, H ∞ performance analysis specifically focuses on evaluating and enhancing the ability of NNs with time delay to maintain stability and desired performance levels despite uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Novel H ∞ performance and delay-dependent exponential passivity for neural networks in response to leakage delay

Singkibud,

Chartbuphapan,

Zamart

et al. 2024

J. Math. Computer Sci.

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This article studies the problem of exponential passivity and H ∞ performance for neural networks (NNs) under the effect of leakage and distributed delays. A novel criterion for achieving exponential passivity in these neural networks is derived. Moreover, we establish new criteria for analyzing the exponential stability and H ∞ performance of the system. Utilizing the Lyapunov-Krasovskii stability theory, we employ an integral inequality to assess the derivative of the Lyapunov-Krasovskii functionals, often referred to as LKFs. This estimation involves constructing novel LKFs that incorporate triple and quadruple integral terms. Furthermore, we obtain results contingent upon the leakage delay and the upper bound of the time-varying delays. To provide context, we conduct comparisons to contrast with existing results. In order to demonstrate the usefulness of the findings, a few numerical examples are provided together with computer simulations.

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The stability of anti-periodic solutions for fractional-order inertial BAM neural networks with time-delays

Zhang¹,

Li²,

Jiang³

et al. 2023

MATH

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<abstract><p>The dynamic signal transmission process can be regarded as an anti-periodic process, and fractional-order inertial neural networks are widely used in signal processing and other fields, so anti-periodicity is also regarded as an important dynamic feature of inertial neural networks. This paper mainly studies the existence and Mittag-Leffler stability of anti-periodic solutions for a class of fractional-order inertial BAM neural networks with time-delays. By introducing variable substitution, the model with two different fractional-order derivatives is transformed into a model with only one fractional-order derivative of the same order. Using the properties of fractional-order calculus, the relationship between the fractional-order integral of the state function with and without time-delays is given. Firstly, the sufficient conditions for the boundedness and the Mittag-Leffler stability of the solutions for the system are derived. Secondly, by constructing the sequence solution of the function for the system and applying Ascoli-Arzela theorem, the sufficient conditions for the existence and Mittag-Leffler stability of the anti-periodic solution are given. Finally, the correctness of the conclusion is verified by a numerical example.</p></abstract>

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Global exponential stability analysis of anti-periodic of discontinuous BAM neural networks with time-varying delays

Cited by 4 publications

References 24 publications

Robust impulse nonlinear delayed multi-agent systems: an exponential synchronization

Robust impulse nonlinear delayed multi-agent systems: an exponential synchronization

Novel H ∞ performance and delay-dependent exponential passivity for neural networks in response to leakage delay

The stability of anti-periodic solutions for fractional-order inertial BAM neural networks with time-delays

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