Finite‐Time Synchronization of Complex‐Valued Delayed Neural Networks with Discontinuous Activations

Wu, Enhua; Yang, Xinsong; Xu, Chen; Alsaadi, Fuad E.; Hayat, Tasawar

doi:10.1002/asjc.1725

Cited by 30 publications

(14 citation statements)

References 42 publications

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“…Time delays are intrinsic characteristics of several industrial processes. Such characteristic can belong to physical properties of processes such as thermal systems (Kuzishchin et al 2019), chemical engineering systems (Pathiran and Jagadeesan 2018), neural network systems (Wu et al 2018), delay communication systems (Wen et al 2016) and largescale interconnected systems (Xiao and Mao 2011;Zhu et al 2005) or also owing to the measurement of system parameters or state variables (Zhou et al 2016). Further, time delays often lead to system instability and beget poor performances, and then, the closed-loop control synthesis and stability analysis of time delay systems are not an straightforward duty, and it is usually considered as a challenging task for the automation control community.…”

Section: Introductionmentioning

confidence: 99%

$$H_{\infty }$$ Optimization-Based Stabilization for Nonlinear Disturbed Time Delay Systems

Tlili

2020

J Control Autom Electr Syst

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This paper contemplates designing a H ∞ dynamic output-feedback decentralized control for nonlinear interconnected disturbed time delay systems. Pursuant to Lyapunov-Krasovskii functional techniques and linear matrix inequality methods, asymptotic stability conditions for the robust stability and stabilization of the studied system are conducted in an optimization problem subject to linear matrix inequalities. The resolution of such a problem enables computing jointly the robust observation and control gain matrices, maximizing the bounds of nonlinear interconnections tolerated by the system without being unstable, improving the performance of the proposed control strategy by minimizing a H ∞ criterion and ensuring the stability of the closed-loop system despite exogenous disturbances applied to the subsystems. Simulation results are provided by a prominent application example of a 3-interconnected machine power system with time delay to demonstrate the efficacy of the designed delay-independent control approach.

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Section: Introductionmentioning

confidence: 99%

$$H_{\infty }$$ Optimization-Based Stabilization for Nonlinear Disturbed Time Delay Systems

Tlili

2020

J Control Autom Electr Syst

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“…Without assuming that the activation function was linear or monotonic, Xu in [9] has given some new sufficient conditions for the complex neural network with discontinuous activations to have at least 16n equilibrium points by using the fixed point theorem, and among which 9n equilibrium points could be locally stable. In [10], the researchers deeply studied the application of discontinuous feature in complex neural network by using the tools of Filippov solutions. On the basis of discontinuity, the influence of nonidentical perturbations was considered in [11], which further extended the previous results.…”

Section: Introductionmentioning

confidence: 99%

Finite-Time and Fixed-Time Stabilization of Discontinuous Neural Networks With Nonlinear Coupling via Switching Approach

2020

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This brief discusses the finite-time and fixed-time stabilization problem of nonsmooth switched neural networks with nonlinear coupling and delays. Base on the framework of Filippov theory, methodology of graph theory and classical Lyapunov functional technique, one can optimize both the finite-time stabilization and fixed-time stabilization issues for complex nonlinear model by utilizing two novel state-feedback controllers and the corresponding adaptive controllers. Moreover, how to shorten the stabilization settling time is discussed in detail when the complex neural network systems are strongly connected, and the influence of initial value on the system is deeply analyzed. Finally, two examples are given to show the advantages and experimental analysis of this method.

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“…Hence, it is worthwhile to further study the dynamical behaviors of this model. Nowadays, considerable outstanding achievements have been published, see [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. Thereinto, adaptive synchronization for complex-valued neural networks is studied in [47].…”

Section: Introductionmentioning

confidence: 99%

Fixed‐time synchronization for complex‐valued BAM neural networks with time delays

Zhang

Guo

et al. 2019

Asian Journal of Control

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In this paper, the fixed‐time synchronization for complex‐valued bidirectional associative memory (BAM) neural networks with time delays is studied. Based on the fixed‐time stability, the Lyapunov functional method and some inequality techniques, a new criterion is presented to guarantee that the addressed systems achieve synchronization in fixed time and a more accurate estimation independent of the initial conditions is given for the settling time. Meanwhile, a new nonlinear delayed controller different from the existing ones is designed. In the end, two numerical examples are provided to illustrate the effectiveness of the obtained result.

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Finite‐Time Synchronization of Complex‐Valued Delayed Neural Networks with Discontinuous Activations

Cited by 30 publications

References 42 publications

$$H_{\infty }$$ Optimization-Based Stabilization for Nonlinear Disturbed Time Delay Systems

$$H_{\infty }$$ Optimization-Based Stabilization for Nonlinear Disturbed Time Delay Systems

Finite-Time and Fixed-Time Stabilization of Discontinuous Neural Networks With Nonlinear Coupling via Switching Approach

Fixed‐time synchronization for complex‐valued BAM neural networks with time delays

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