Projective synchronization of fractional-order delayed neural networks based on the comparison principle

This paper addresses finite-time projective synchronization of stochastic complex-valued neural networks (SCVNNs) with probabilistic time-varying delays (PTVs). First, in the complex domain, PTVs are introduced into the studied model and a novel feedback control scheme is constructed. Next, based on inequalities techniques and the Lyapunov stability approach, some novel projective synchronization criteria are established by decomposing SCVNNs into two equivalent real-valued systems. Moreover, a setting time function is created by employing lemma 4. Compared with previous researches, our theory content is an extension and complement to known results. Finally, numerical simulation is presented to validate the effectiveness of theoretical analysis results. INDEX TERMS Projective synchronization; Finite-time; Probabilistic time-varying delays; Stochastic complex-valued neural networks.

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“…is similar to g mn (t, u m (t), u m (t − τ (t))), the corresponding equations are omitted. (16) and (17) under control (18),…”

Section: Numerical Simulationmentioning

confidence: 99%

“…Under control scheme (18), the state trajectories of system (16) and (17) are presented in Fig.1 and Fig.2. Obviously, we can get that SCVNNs (16) and 17 synchronization, where the projective coefficient λ = 2.…”

Section: Numerical Simulationmentioning

confidence: 99%

Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks With Probabilistic Time-Varying Delays

et al. 2021

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“…x n (t)) -Ĥ n (η * n ,ȳ n (t)) satisfies |ε n (ȳ n (t), x n (t))| ≤ ε * n with ε * n > 0 being a known constant. To accomplish the remaining protocol design procedure, the active controller may be constructed as in the following expression: 27) where k 1n > 0, k 2n ≥ ε * n and k 3n ≥ d n . Multiply both sides of (4.24) with n (t).…”

Section: Controller Construction and Stability Analysismentioning

confidence: 99%

“…The goal of synchronization is to design an active controller to synchronize the so-called slave dynamical system with another diverse one, namely the master. Various synchronization protocols have been proposed, including lag synchronization [26], projective synchronization [27], fixed time synchronization [28], and chaos synchronization [29,30]. In essence, chaos synchronization generalizes chaos control [31], which enables the chaotic master-slave error dynamics trajectories to be asymptotically stable.…”

Section: Introductionmentioning

confidence: 99%

A hybrid adaptive synchronization protocol for nondeterministic perturbed fractional-order chaotic nonlinear systems

Lin

Xue

et al. 2020

Adv Differ Equ

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In this paper, we investigate hybrid adaptive synchronization issue for a class of perturbed fractional-order chaotic systems with nondeterministic nonlinear terms. On the basis of fractional-order extended version of Lyapunov stability criterion, a novel fuzzy adaptive synchronization control protocol coupled with backstepping-based method is constructed, ensuring that the synchronization errors converge to a sufficiently small region of the origin. In order to avert the occurrence of "explosion of complexity", we take advantage of a fuzzy logic system to estimate the unknown systematic term approximately in every backstepping step. Finally, some numerical simulations are given to exemplify the effectiveness of the proposed approach.

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“…For inner synchronization, it is a collective behavior within the network and most of the researchers have focused on this type [15,16]. For outer synchronization, it is a collective behavior between two or more networks [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Mixed $H_{\infty }/$passive exponential function projective synchronization of delayed neural networks with hybrid coupling based on pinning sampled-data control

et al. 2019

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This paper presents the problem of mixed H ∞ /passive exponential function projective synchronization of delayed neural networks with constant discrete and distributed delay couplings under pinning sampled-data control scheme. The aim of this work is to focus on designing of pinning sampled-data controller with an explicit expression by which the stable synchronization error system is achieved and a mixed H ∞ /passive performance level is also reached. Particularly, the control method is designed to determine a set of pinned nodes with fixed coupling matrices and strength values, and to select randomly pinning nodes. To handle the Lyapunov functional, we apply some new techniques and then derive some sufficient conditions for the desired controller existence. Furthermore, numerical examples are given to illustrate the effectiveness of the proposed theoretical results.

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Projective synchronization of fractional-order delayed neural networks based on the comparison principle

Cited by 23 publications

References 33 publications

Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks With Probabilistic Time-Varying Delays

Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks With Probabilistic Time-Varying Delays

A hybrid adaptive synchronization protocol for nondeterministic perturbed fractional-order chaotic nonlinear systems

Mixed $H_{\infty }/$passive exponential function projective synchronization of delayed neural networks with hybrid coupling based on pinning sampled-data control

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