Exponential Lag Synchronization of Memristive Neural Networks with Reaction Diffusion Terms via Neural Activation Function Control and Fuzzy Model

Liu, Yicheng; Liao, Xiaofeng; Li, Chuandong

doi:10.1002/asjc.1862

Cited by 4 publications

(3 citation statements)

References 39 publications

(57 reference statements)

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“…Theorem 1. Under Assumptions 1, 2, and 3, using the distributed piecewise event-triggered mechanism (equation (23)) and the sampling controller (equation ( 21)), the master-slave networks systems (equations ( 1) and ( 3)) can be output quasisynchronized within the error Θ, and the error system (equation (18)) converges exponentially to the error level Θ with the convergence index κ 2 > 0, if the criteria are satisfied:…”

Section: Synchronization Analysismentioning

confidence: 99%

“…Until now, many worthwhile results about MRDNNs have been achieved. Global synchronization [21,22], exponential Lag synchronization [23], and fixed-time synchronization [24] of MRDNNs have been studied. Nonetheless, the mismatch of the master-slave system due to state-dependent parameters inevitably causes the synchronization error of the collective behaviour to change in a certain range.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Event‐Triggered Output Synchronization of Complex‐Valued Memristive Reaction‐Diffusion Complex Networks with Spatial Sampled‐Data

Chen

Cheng

2021

Complexity

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This study addresses the problem of output quasisynchronization for coupled complex-valued memristive reaction-diffusion complex networks via the distributed event-triggered control scheme. First, by using the separate method, set value mapping, and intermediate value theorem, the complex-valued memristive reaction-diffusion complex networks can be transferred into two semi-uncertain real-valued reaction-diffusion complex networks. Second, a distributed output piecewise event-triggered control (OPETC) scheme with spatial sampled-data is first proposed including a spatial sampling event-triggered generator and spatiotemporal sampling state feedback controller. Furthermore, this scheme can effectively save the measurement resources and lower the update rate of controllers in spatial and time domain. Third, the synchronization analysis is considered by utilizing an appropriate Lyapunov function, the Halanay inequality, and the improved Wirtinger inequality. Subsequently, several output event-triggered quasisynchronization criteria are derived. The relations among event trigger conditions, spatial sampling interval, convergence rate, and control gain are given by rigorous mathematical derivation. Finally, multiple simulations are compared to substantiate the validation of the OPETC scheme.

show abstract

Section: Synchronization Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Event‐Triggered Output Synchronization of Complex‐Valued Memristive Reaction‐Diffusion Complex Networks with Spatial Sampled‐Data

Chen

Cheng

2021

Complexity

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show abstract

“…Neural network is a kind of special complex network, which can be used to describe many familiar systems, such as associative memory, neural learning, and artificial NNs [1,2]. With the continuous in-depth study of NNs knowledge, the synchronization of NNs has gradually come into the public view [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Fixed‐time synchronization of neural networks with time delay via quantized intermittent control

Yang

Huang

Wen

et al. 2022

Asian Journal of Control

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This paper presents an approach for fixed‐time synchronization (FIXTS) of neural networks (NNs) by designing quantized intermittent controller. Under the intermittent controller, the synchronization between neural network systems with time delay can be realized. Based on intermittent strategy, FIXTs theory is proposed, and a sufficient condition is established to realize the FIXTS of the master–slave NNs. At the same time, the establishment time of FIXTS is estimated. Finally, the simulation of Gilli attractor to prove the validity of the proposed method.

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Novel Caputo fractional differential approach and hybrid control scheme for finite‐time synchronization of nonidentical delayed reaction–diffusion neural networks

Zhang,

Wang,

et al. 2024

Asian Journal of Control

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This work focuses on the finite‐time synchronization (F‐TS) for nonidentical delayed neural networks (DNNs) including Caputo fractional partial differential operator and reaction–diffusion terms. A novel F‐TS lemma is proposed by constructing a new Caputo differential inequality. Applying the new lemma and designing two hybrid controllers with time delay and the sign function, the F‐TS criteria on the introduced Caputo reaction–diffusion nonidentical DNNs under the Neumann boundary condition are derived by making use of the Filippov differential inclusion, Green's theorem, and fractional Razumikhin theorem. The conditions are expressed through the algebraic inequality which can greatly decrease the computation in checking the F‐TS performance. Moreover, the validity and correctness of the F‐TS results are illustrated by selecting various orders, spatial positions, and diffusion parameters.

show abstract

Exponential Lag Synchronization of Memristive Neural Networks with Reaction Diffusion Terms via Neural Activation Function Control and Fuzzy Model

Cited by 4 publications

References 39 publications

Distributed Event‐Triggered Output Synchronization of Complex‐Valued Memristive Reaction‐Diffusion Complex Networks with Spatial Sampled‐Data

Distributed Event‐Triggered Output Synchronization of Complex‐Valued Memristive Reaction‐Diffusion Complex Networks with Spatial Sampled‐Data

Fixed‐time synchronization of neural networks with time delay via quantized intermittent control

Novel Caputo fractional differential approach and hybrid control scheme for finite‐time synchronization of nonidentical delayed reaction–diffusion neural networks

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