Finite-Time and Fixed-Time Synchronization of Inertial Neural Networks with Mixed Delays

Aouiti, Chaouki; Assali, El Abed

doi:10.1007/s11424-020-9029-8

Cited by 35 publications

(4 citation statements)

References 54 publications

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“…As a dynamic behavior of coupled systems, synchronization has been widely studied by many scholars (Chaouki and Abed, 2021; Ding et al, 2016; Liu et al, 2012; Xu et al, 2021b; Zhang et al, 2018b, 2021). Synchronization mainly includes finite, infinite and fixed-time synchronization (F-TS), among which F-TS has good practical value, and its solution time in the case of initial synchronization error can be calculated directly.…”

Section: Introductionmentioning

confidence: 99%

Finite/fixed-time synchronization control of fuzzy inertial cellular neural networks with mixed delays

Yang

Zou

Liu

2023

Transactions of the Institute of Measurement and Control

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In this paper, the finite-time (FT) and fixed-time synchronization (F-TS) problems of fuzzy inertial cellular neural networks (FICNNs) with mixed delays are discussed based on the Filippov solution theory and FT stability theory. Compared with related research, this paper considers the fuzzy inertial system combined with discontinuous activation of cellular neural networks (CNNs) for the first time. Then, by choosing appropriate variable transformation, the original system can be rewritten as a first-order differential system. Some novel and useful sufficient conditions for finite/fixed-time synchronization of FICNNs are established by applying three suitable discontinuous state feedback controllers. Finally, three numerical simulations are presented to elaborate the validity of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Finite/fixed-time synchronization control of fuzzy inertial cellular neural networks with mixed delays

Yang

Zou

Liu

2023

Transactions of the Institute of Measurement and Control

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“…Zhang et al [13] proposed a class of statedependent switching neural networks with inertial terms and distributed time delays. Chaouki et al [14] proposed the driver response synchronization in a finite time and a fixed time of an inertial neural network with time-varying and distributed time-delay. Chen et al [15] considered the fixed-time synchronization control of inertial memristor-based neural networks with discrete delay.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis and Synchronization Control of Fractional-Order Inertial Neural Networks With Time-Varying Delay

2022

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This paper mainly investigates the stability analysis and synchronization control of a fractional-order time-varying delay inertial neural network. Firstly, a time-varying delay inertial neural network model is established, which is easy to implement in engineering applications. Secondly, based on the properties of the Caputo fractional derivative and the proposed lemma, the original inertial system is transferred into conventional system through the proper variable substitution, and a synchronous control strategy for the time varying delay inertial network is then established. In addition, the stability conditions of a class of Caputo fractional-order time-delay inertial neural networks are given. Finally, three simulation examples are given to verify the rationality and effectiveness of the method proposed in this paper.

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“…It is widely known that neural network systems own great application value in plenty of areas such as disease treatment, pattern recognition, intelligent control, biology, information processing, control technique, and so on [1][2][3][4][5][6]. In real world, time delay usually occurs in lots of neural networks and biological systems since there is delay of information transmission of distinct neurons and the response of diverse biotic populations.…”

Section: Introductionmentioning

confidence: 99%

“…In [29], Karaoglu et al took kernel function as case (2). By means of stability criterion and Hopf bifurcation theory of delayed differential equation, Karaoglu et al obtained a sufficient condition to guarantee the stability and the onset of Hopf bifurcation of system (1). In addition, with the aid of center manifold theorem and normal form theory, the concrete formula to determine bifurcation nature is given.…”

Section: Introductionmentioning

confidence: 99%

Periodic Oscillatory Phenomenon in Fractional-Order Neural Networks Involving Different Types of Delays

Wang

Liu

2021

Mathematical Problems in Engineering

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This research is chiefly concerned with the stability and Hopf bifurcation for newly established fractional-order neural networks involving different types of delays. By means of an appropriate variable substitution, equivalent fractional-order neural network systems involving one delay are built. By discussing the distribution of roots of the characteristic equation of the established fractional-order neural network systems and selecting the delay as bifurcation parameter, a novel delay-independent bifurcation condition is derived. The investigation verifies that the delay is a significant parameter which has an important influence on stability nature and Hopf bifurcation behavior of neural network systems. The computer simulation plots and bifurcation graphs effectively illustrate the reasonableness of the theoretical fruits.

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Finite-Time and Fixed-Time Synchronization of Inertial Neural Networks with Mixed Delays

Cited by 35 publications

References 54 publications

Finite/fixed-time synchronization control of fuzzy inertial cellular neural networks with mixed delays

Finite/fixed-time synchronization control of fuzzy inertial cellular neural networks with mixed delays

Stability Analysis and Synchronization Control of Fractional-Order Inertial Neural Networks With Time-Varying Delay

Periodic Oscillatory Phenomenon in Fractional-Order Neural Networks Involving Different Types of Delays

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