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This article studies the global exponential synchronization (GES) of quaternion-valued memristive delayed neural networks (QVMDNNs) by quantized intermittent control (QIC). Without decomposing the original systems into usual real-valued or complex-valued ones, the discussed system is directly processed in both cases of the differential inclusion theory. Based on two QIC strategies and applying Lyapunov functional method and inequality techniques, several new delay-dependent criteria in the form of real-valued algebraic inequalities are directly derived to assure the GES of the concerned system. Compared to the traditional feedback control, the QIC strategy can reduce the control expenses and shorten time to achieve the GES. Ultimately, two examples are given to validate the effectiveness and advantages of the proposed outcomes. • The synchronization of quaternion-valued memristive delayed neural networks is studied. • The concerned networks are directly analyzed as a whole without any decomposition. • Some delay-dependent criteria are obtained to ensure the global exponential synchronization. • The comparison of the quantized intermittent controllers has been done using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

This paper studies the fixed-time synchronization (FDTS) of complex-valued neural networks (CVNNs) based on quantized intermittent control (QIC) and applies it to image protection and 3D point cloud information protection. A new controller was designed which achieved FDTS of the CVNNs, with the estimation of the convergence time not dependent on the initial state. Our approach divides the neural network into two real-valued systems and then combines the framework of the Lyapunov method to give criteria for FDTS. Applying synchronization to image protection, the image will be encrypted with a drive system sequence and decrypted with a response system sequence. The quality of image encryption and decryption depends on the synchronization error. Meanwhile, the depth image of the object is encrypted and then the 3D point cloud is reconstructed based on the decrypted depth image. This means that the 3D point cloud information is protected. Finally, simulation examples verify the efficacy of the controller and the synchronization criterion, giving results for applications in image protection and 3D point cloud information protection. [ABSTRACT FROM AUTHOR]

This article investigates exponential synchronization of complex-valued memristor-based delayed neural networks (CVMDNNs). Firstly, the CVMDNNs are divided into two real-valued systems: the real and the imaginary part. These two parts are not isolated, they are interrelated. Secondly, integrating the theory of differential inclusion with the definition of Filippov solution, the interval parameter system is established, and the synchronization problem of CVMDNNs is studied. Thirdly, an innovative quantized intermittent controller is designed that takes full advantage of the networks' transmission capacity and reduces control costs. Moreover, the exponential synchronization sufficient condition of CVMDNNs can be obtained via applying the Lyapunov function technique and quantized intermittent control. Finally, an explanatory illustration is offered to certify the validity of the theoretical outcomes. [ABSTRACT FROM AUTHOR]

This paper considers the finite-time drive-response synchronization of stochastic nonlinear systems consisting of continuous-time and discrete-time subsystems. To save communication resources and reduce control cost, quantized controllers, which only work on continuous-time intervals, are designed. Owing to the hybrid characteristics of continuous- and discrete-time subsystems, existing finite-time stability theorems are not applicable. By developing novel analytical techniques, three criteria are derived to guarantee the finite-time synchronization. Moreover, the settling time is explicitly estimated. It is shown that the settling time is dependent not only on the control gains and systems’ initial conditions, but also on the control width and uncontrolled width. Numerical examples demonstrate the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper considers the fixed-time synchronization (FIXTS) of neural networks (NNs) by using quantized intermittent control (QIC). Based on QIC, a fixed-time controller is designed to ensure that the NNs achieve synchronization in finite time. With this controller, the settling time can be estimated regardless of initial conditions. After ensuring that the system has stabilized through this strategy, it is suitable for image protection given the behavior of the system. Meanwhile, the encryption effect of the image depends on the encryption algorithm, and the quality of the decrypted image depends on the synchronization error of NNs. The numerical results show that the designed controller is effective and validate the practical application of FIXTS of NNs in image protection.

Abstract It is well known that how to deal with the effect of time delay and how to determine the control and rest widths are the main difficulties for intermittent control. This paper considers asymptotic synchronization of drive-response memristive neural networks (MNNs) with bounded time-varying discrete delay and unbounded distributed delay (mixed delays), which extends existing intermittent control techniques and reveals new relationship between control width and rest width. A quantized intermittent control (QIC) is designed to save both channel resources and control cost and reduce both the amount of transmitted information and channel blocking. Based on weighted double-integral inequalities, novel Lyapunov–Krasovskii functionals with negative terms are designed, which reduce the conservativeness of obtained results greatly. Sufficient conditions in terms of linear matrix inequalities (LMIs) are obtained to ensure the asymptotic synchronization. The control gains can also be designed by solving the LMIs. It is shown that the QIC can be neither periodic nor proportional between control width and rest width. Moreover, the relationships between control width, rest width, and convergence rate are explicitly given. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper focuses on stochastic exponential synchronization of delayed memristive neural networks (MNNs) by the aid of systems with interval parameters which are established by using the concept of Filippov solution. New intermittent controller and adaptive controller with logarithmic quantization are structured to deal with the difficulties induced by time-varying delays, interval parameters as well as stochastic perturbations, simultaneously. Moreover, not only control cost can be reduced but also communication channels and bandwidth are saved by using these controllers. Based on novel Lyapunov functions and new analytical methods, several synchronization criteria are established to realize the exponential synchronization of MNNs with stochastic perturbations via intermittent control and adaptive control with or without logarithmic quantization. Finally, numerical simulations are offered to substantiate our theoretical results. [ABSTRACT FROM AUTHOR]

This paper considers the fixed-time synchronization (FIXTS) of neural networks (NNs) by using quantized intermittent control (QIC). Based on QIC, a fixed-time controller is designed to ensure that the NNs achieve synchronization in finite time. With this controller, the settling time can be estimated regardless of initial conditions. After ensuring that the system has stabilized through this strategy, it is suitable for image protection given the behavior of the system. Meanwhile, the encryption effect of the image depends on the encryption algorithm, and the quality of the decrypted image depends on the synchronization error of NNs. The numerical results show that the designed controller is effective and validate the practical application of FIXTS of NNs in image protection. [ABSTRACT FROM AUTHOR]