Asymptotic Synchronization of Memristive Cohen-Grossberg Neural Networks with Time-Varying Delays via Event-Triggered Control Scheme

Yao, Wei; Yu, Fei; Zhang, Jin; Ling, Zhou

doi:10.3390/mi13050726

Cited by 9 publications

(6 citation statements)

References 50 publications

(114 reference statements)

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“…Remark 1 Compared with the existing results [43,[51][52][53], the following are the key elements and advantages of this paper:…”

Section: Practically Exponential Input-to-state Stabilization Of Srdd...mentioning

confidence: 87%

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Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Gokulakrishnan

Sriniva

2023

Preprint

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The stabilization problem for a class of stochastic reaction-diffusion delayed Cohen-Grossberg neural networks (SRDDCGNNs) with event-triggered controller is addressed in this paper. To address such a problem, Neumann boundary condition, distributed and boundary external disturbances are introduced. New sufficient criteria are derived by using the Lyapunov method, event-triggered mechanism, and the linear matrix inequality (LMI) approach to ensure the proposed controlled systems achieve practically exponential input-to-state stabilization. In light of these criteria, the impact of an event-triggered controller on practically exponential input-to-state stability (PEISS) is examined. Furthermore , the obtained results are successfully applied to stochastic reaction-diffusion delayed cellular neural networks (SRDDCNNs) and stochastic reaction-diffusion delayed Hopfield neural networks (SRD-DHNNs). At last, simulation results are given to illustrate the main results, and the SRDDHNNs are applied to the image encryption.

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“…Remark 1 Compared with the existing results [43,[51][52][53], the following are the key elements and advantages of this paper:…”

Section: Practically Exponential Input-to-state Stabilization Of Srdd...mentioning

confidence: 87%

“…Especially, in [51], the author studied the fixedtime synchronization of inertial CGNNs via event-triggered control. In [52], the author studied the asymptotic synchronization of memristive CGNNs via event-triggered control. In [53], the author studied the ISS of stochastic fuzzy CGNNs via event-triggered control.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Gokulakrishnan

Sriniva

2023

Preprint

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“…In recent years, some research studies on artificial neural networks and their applications [24][25][26][27][28][29] have been widely discussed. With the creation of the first memristor [30], many researchers have used memristors to simulate synapses [31][32][33] between neurons in the human brain and to analyze the dynamical behavior [34][35][36][37][38] of artificial neuronal networks.…”

Section: Introductionmentioning

confidence: 99%

An image encryption algorithm based on a 3D chaotic Hopfield neural network and random row–column permutation

et al. 2023

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This study proposes a novel color image encryption algorithm based on a 3D chaotic Hopfield neural network and random row–column permutation. First, a 3D chaotic Hopfield neural network is proposed to produce the random sequence for generating the diffusion and permutation keys. Then, the rows and columns of the original image are randomly arranged according to the permutation key in the permutation process. Three subgraphs are formed by separating the R, G, and B components of the color image in the diffusion process. Each of the three subgraphs is split along the columns to form three parts; the left and middle parts are exchanged. Three diffusion keys are used to encrypt each of the three parts. Finally, the individually encrypted subgraphs are stitched together to obtain the final encrypted image. Simulation results using MATLAB and FPGA and security analysis demonstrate that the encryption scheme has good performance.

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“…These theories provide a suitable theoretical foundation for further study on mathematical memristors. Also, since many phenomena in reality are multistable, recently, there have been many studies on multistable memristor systems [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Dynamics analysis and image encryption application of Hopfield neural network with a novel multistable and highly tunable memristor

Yao,

Liu,

Sun

et al. 2023

Nonlinear Dyn

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Building neural network models and studying their dynamic behaviours is extremely important from both a theoretical and practical standpoint due to the rapid advancement of artificial intelligence. In addition to its engineering applications, this article concentrates primarily on the memristor model and chaotic dynamics of the asymmetric memristive neural network. First, we develop a novel-multistable, highly-tunable memristor model. Using this memristor model to build an asymmetric memristive neural network (AMNN), the chaotic dynamics of the proposed AMNN are investigated and analyzed using fundamental dynamics techniques such as equilibrium stability, bifurcation diagrams, and Lyapunov exponents. According to the findings of this study, the proposed AMNN possesses a number of complex dynamic properties, including scaling amplitude hyperchaos with coupling strength control, and coexisting unusual chaotic attractors with ini-

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Asymptotic Synchronization of Memristive Cohen-Grossberg Neural Networks with Time-Varying Delays via Event-Triggered Control Scheme

Cited by 9 publications

References 50 publications

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

An image encryption algorithm based on a 3D chaotic Hopfield neural network and random row–column permutation

Dynamics analysis and image encryption application of Hopfield neural network with a novel multistable and highly tunable memristor

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