Global exponential stabilization and lag synchronization control of inertial neural networks with time delays

Shi, Jichen; Zeng, Zhigang

doi:10.1016/j.neunet.2020.03.006

Cited by 71 publications

(38 citation statements)

References 45 publications

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“…This fact can further show that the delays can affect the FTS of INNs. Thus, the established results in this paper can include and extend the previous works on the INNs based on the delay-independent criteria such as [20,21].…”

Section: Finite-time Stabilization Analysissupporting

confidence: 59%

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Finite-time stabilization of discontinuous fuzzy inertial Cohen–Grossberg neural networks with mixed time-varying delays

Kong

Zhu

Sakthivel

2021

NAMC

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This article aims to study a class of discontinuous fuzzy inertial Cohen–Grossberg neural networks (DFICGNNs) with discrete and distributed time-delays. First of all, in order to deal with the discontinuities by the differential inclusion theory, based on a generalized variable transformation including two tunable variables, the mixed time-varying delayed DFICGNN is transformed into a first-order differential system. Then, by constructing a modified Lyapunov–Krasovskii functional concerning with the mixed time-varying delays and designing a delayed feedback control law, delay-dependent criteria formulated by algebraic inequalities are derived for guaranteeing the finite-time stabilization (FTS) for the addressed system. Moreover, the settling time is estimated. Some related stability results on inertial neural networks is extended. Finally, two numerical examples are carried out to verify the effectiveness of the established results.

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Section: Finite-time Stabilization Analysissupporting

confidence: 59%

“…So, stability analysis of the INNs is necessary. In recent years, stability of INNs model and its generalizations have been widely considered; see [7,13,[19][20][21].…”

Section: Previous Workmentioning

confidence: 99%

Finite-time stabilization of discontinuous fuzzy inertial Cohen–Grossberg neural networks with mixed time-varying delays

Kong

Zhu

Sakthivel

2021

NAMC

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“…According to the Lyapunov stability theory, k 1,2 < a 1 − 7, k 3,4 < −7 − |a 2 |/2, and k 5 < −7 − |a 3 |/2, which make the error system asymptotically stable. The master system (2), the corresponding slave system (11), and the controller (12) thus complete the STDS.…”

Section: Design Of the Stds Controllermentioning

confidence: 99%

“…System (16) was used as an L1 transmitter, system (11) was used as an L2 receiver, and system (17) was used as a controller for the MATLAB simulation. The initial values of the system were set to u(0) [1,2,3 .…”

Section: Speech Communication Simulationmentioning

confidence: 99%

“…The synchronization of complex dynamic chaotic systems can not only reveal many natural phenomena, but also has many applications in image processing, security communication, and mechanical engineering (see [1][2][3][4][5]). Many different synchronization modes have been well-studied, such as combination synchronization, phase synchronization, projection synchronization, and lag synchronization, and many important results have been obtained (see [6][7][8][9][10][11]). A chaotic system with time-delay is better for describing a real physical process than a chaotic system without time-delay.…”

Section: Introductionmentioning

confidence: 99%

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Time-Delay Characteristics of Complex Lü System and Its Application in Speech Communication

Guo

Wang

et al. 2020

Entropy

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Although complex Lü systems have been considered in many studies, application of the self-time-delay synchronization (STDS) of complex Lü systems in secure speech communications does not appear to have been covered in much of the literature. Therefore, it is meaningful to study the STDS of complex Lü systems and its application in secure speech communication. First, a complex Lü system with double time-delay is introduced and its chaotic characteristics are analyzed. Second, a synchronization controller is designed to achieve STDS. Third, the improved STDS controller is used to design a speech communication scheme based on a complex Lü system. Finally, the effectiveness of the controller and communication scheme are verified by simulation.

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Non‐fragile state estimation for second‐order memristive neural networks with unbounded time‐varying delays

Luo

Jian

Zheng

2021

Adaptive Control & Signal

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Without converting the second-order memristive neural networks (SMNNs) into the usual first-order systems, this article works on the non-fragile state estimation of SMNNs with unbounded time-varying delays. The memristor is treated as uncertainty by recommending some measurable functions. According to Lyapunov functional approach, inequality techniques, and combining Barbalat lemma, a new Lyapunov functional is proposed to straightway discuss the asymptotic stability for the error system, and several sufficient conditions are obtained to assure the existence of the preset estimators. The nonfragile estimators are designed and several explicit descriptions of the estimators are given by solving linear matrix inequalities. To overcome the uncertainty of state variables, the problem of parameter mismatch is as well considered. Finally, two examples are given to verify the availability of the results.

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Global exponential stabilization and lag synchronization control of inertial neural networks with time delays

Cited by 71 publications

References 45 publications

Finite-time stabilization of discontinuous fuzzy inertial Cohen–Grossberg neural networks with mixed time-varying delays

Finite-time stabilization of discontinuous fuzzy inertial Cohen–Grossberg neural networks with mixed time-varying delays

Time-Delay Characteristics of Complex Lü System and Its Application in Speech Communication

Non‐fragile state estimation for second‐order memristive neural networks with unbounded time‐varying delays

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