Adaptive synchronization of delayed reaction-diffusion neural networks with unknown non-identical time-varying coupling strengths

Li, Junmin; He, Chao; Zhang, Weiyuan; Chen, Minglai

doi:10.1016/j.neucom.2016.09.006

Cited by 15 publications

(4 citation statements)

References 38 publications

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“…Due to these potential engineering applications, the studies of synchronization on delayed neural networks has recently attracted significant attentions. Besides, since [10] presented the drive-response concept, and to obtain synchronization behaviors of delayed neural networks, many advanced control strategies have been reported, such as impulsive control [11][12][13], intermittent control [14,15], sampled-data control [16][17][18][19], pinning control [20,21], adaptive control [22][23][24], sliding mode control (SMC) [25][26][27][28][29], and others. In addition, for the sake of acquiring the faster and more precise synchronization behaviors, finite-time synchronization [30][31][32][33][34] and fixed-time synchronization [35][36][37][38][39][40] for various types of network systems has been intensively studied.…”

Section: Introductionmentioning

confidence: 99%

Improved Integral Sliding Mode Control for Adaptive Synchronization of Delayed Recurrent Neural Networks

Xu,

Xiong

2023

Preprint

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This paper is devoting to addressing the problem of adaptive synchronization for recurrent neural networks with time-varying delays, in which the unknown parameters are taken into account. A new and simple integral sliding mode control (SMC) strategy is developed motivated by the previous idea of the existing literature. Based on drive-response concept, the synchronization error system is firstly derived such that the corresponding synchronization error signal can be applied to construct a simple integral sliding mode manifold. Then, an appropriate sliding mode controller with adaptive laws is designed such that the reachability of the predefined manifold is ensured and thereafter the synchronization problem between the response system and a drive system with unknown parameters is addressed. Finally, two numerical examples are given to substantiate the effectiveness of the presented control strategy.

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Section: Introductionmentioning

confidence: 99%

Improved Integral Sliding Mode Control for Adaptive Synchronization of Delayed Recurrent Neural Networks

Xu,

Xiong

2023

Preprint

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“…Most of the literatures mentioned above are concerned with unknown nonlinear dynamical systems with constant parameterization. Then, Li et al [36][37][38] gave some results on the synchronization of nonlinear reaction-diffusion NNs with unknown TVP. However, in these results, the unknown TVPs in the studied systems were linearly ones rather than nonlinearly ones.…”

Section: Introductionmentioning

confidence: 99%

“…(ii) Unlike PDEs with TVP studied in refs. [36][37][38], this paper gives an ANNC scheme for the uncertain parabolic DPSs with NPTVP, which has a wider application in actual engineering modeling.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural networks control for uncertain parabolic distributed parameter systems with nonlinear periodic time-varying parameter

Lei

Li²,

Zhao³

2022

Sci. China Technol. Sci.

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This paper studies the problem of adaptive neural networks control (ANNC) for uncertain parabolic distributed parameter systems (DPSs) with nonlinear periodic time-varying parameter (NPTVP). Firstly, the uncertain nonlinear dynamic and unknown periodic TVP are represented by using neural networks (NNs) and Fourier series expansion (FSE), respectively. Secondly, based on the ANNC and reparameterization approaches, two control algorithms are designed to make the uncertain parabolic DPSs with NPTVP asymptotically stable. The sufficient conditions of the asymptotically stable for the resulting closed-loop systems are also derived. Finally, a simulation is carried out to verify the effectiveness of the two control algorithms designed in this work.

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“…Therefore, the synchronization problem of complex networks has elicited extensive attention and has become an important research topic. Many important results on synchronization have been obtained for various complex dynamical networks . Most existing results have been utilized to guarantee the asymptotic or exponential synchronization of various systems.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time synchronization of a class of N‐coupled complex partial differential systems with time‐varying delay

Xia

Luo

Zhou³

2019

Math Methods in App Sciences

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This study examines finite-time synchronization for a class of N-coupled complex partial differential systems (PDSs) with time-varying delay. The problem of finite-time synchronization for coupled drive-response PDSs with timevarying delay is similarly considered. The synchronization error dynamic of the PDSs is defined in the q-dimensional spatial domain. We construct a feedback controller to achieve finite-time synchronization. Sufficient conditions are derived by using the Lyapunov-Krasoviskii stability approach and inequalities technology to ensure that the proposed networks achieve synchronization in finite time. The proposed systems demonstrate extensive application. Finally, an example is used to verify the theoretical results.

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Adaptive synchronization of delayed reaction-diffusion neural networks with unknown non-identical time-varying coupling strengths

Cited by 15 publications

References 38 publications

Improved Integral Sliding Mode Control for Adaptive Synchronization of Delayed Recurrent Neural Networks

Improved Integral Sliding Mode Control for Adaptive Synchronization of Delayed Recurrent Neural Networks

Adaptive neural networks control for uncertain parabolic distributed parameter systems with nonlinear periodic time-varying parameter

Finite‐time synchronization of a class of N‐coupled complex partial differential systems with time‐varying delay

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