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This article aims to study fixed-time projective lag synchronization(FXPLS) and preassigned-time projective lag synchronization(PTPLS) of hybrid inertial neural networks(HINNs) with state-switched and discontinuous activation functions(DAFs). By constructing new hybrid fixed-time control and based on theory of non-smooth analysis, we achieve novel results on FXPLS for such HINNs. Through designing novel hybrid preassigned-time control, new criteria on PTPLS of the HINNs is also taken into account. And as distinct from recent works, the FXPLS and PTPLS results are established via non-variable substitution and in a more generalized framework than common synchronization, which also has more extensive practical applications. Finally, example simulations are displayed to set forth the validity of the acquired FXPLS and PTPLS. [ABSTRACT FROM AUTHOR]

This paper presents a class of state-dependent switched neural networks with inertial items and distributed delays. Several new results are derived to ensure the exponential synchronization of such switched neural networks by using a novel hybrid control scheme and the Lyapunov stability theory. Finally, simulations are given to show the validity of the derived results. We believe the new useful control method of this paper widens the application scope for the switched neural networks. [ABSTRACT FROM AUTHOR]

In this brief, we investigate a class of second-order memristive neural networks (SMNNs) with mixed time-varying delays. Based on nonsmooth analysis, the Lyapunov stability theory, and adaptive control theory, several new results ensuring global stabilization of the SMNNs are obtained. In addition, compared with the reduced-order method used in the existing research studies, we consider the global stabilization directly from the SMNNs themselves without the reduced-order method. Finally, we give some numerical simulations to show the effectiveness of the results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the global exponential stabilization of memristor-based chaotic neural networks with both time-varying delays and general activation functions. Here, we adopt nonsmooth analysis and control theory to handle memristor-based chaotic neural networks with discontinuous right-hand side. In particular, several new sufficient conditions ensuring exponential stabilization of memristor-based chaotic neural networks are obtained via periodically intermittent control. In addition, the proposed results here are easy to verify and they also extend the earlier publications. Finally, numerical simulations illustrate the effectiveness of the obtained results. [ABSTRACT FROM PUBLISHER]

Abstract: The paper presents theoretical results on the global exponential periodicity and stability of a class of memristor-based recurrent neural networks with multiple delays. The dynamic analysis in the paper employs the theory of differential equations with discontinuous right-hand side as introduced by Filippov. By using the inequality techniques and a useful Lyapunov functional, some new testable algebraic criteria are obtained for ensuring the existence and global exponential stability of periodic solution of the system. The model based on the memristor widens the application scope for the design of neural networks, and the new effective results also enrich the toolbox for the qualitative analysis of neural networks. [Copyright &y& Elsevier]

This paper deals with the fixed-time stabilization (FXTS) and preassigned-time stabilization (PATS) problems for delayed memristive neural networks (DMNNs) via a unified controller. Firstly, we design a delayed controller to realize the FXTS of DMNNs. Moreover, the sufficient conditions and accurate time estimation of FXTS are presented. It is proved that the system states can achieve stabilization within a fixed time without being affected by the initial states. Then, by modifying the parameters of the same controller, DMMNs achieve stabilization within an arbitrary preassigned time, which gets rid of the limitation of system and controller parameters. Finally, the feasibility and validity of the main results are verified by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper deals with the fixed-time stabilization (FXTS) and preassigned-time stabilization (PATS) problems for delayed memristive neural networks (DMNNs) via a unified controller. Firstly, we design a delayed controller to realize the FXTS of DMNNs. Moreover, the sufficient conditions and accurate time estimation of FXTS are presented. It is proved that the system states can achieve stabilization within a fixed time without being affected by the initial states. Then, by modifying the parameters of the same controller, DMMNs achieve stabilization within an arbitrary preassigned time, which gets rid of the limitation of system and controller parameters. Finally, the feasibility and validity of the main results are verified by numerical simulations. [ABSTRACT FROM AUTHOR]

Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results. [ABSTRACT FROM PUBLISHER]

Boolean chaos generated by autonomous Boolean networks has been successfully applied to random number generation, physical unclonable function, gene regulation and reservoir computing, because it can generate complex dynamics with very simple structure. Influences of noise on autonomous Boolean networks chaotic system in delay time parameter spaces are analyzed by simulation and experiments in this article. It is found that the influences of noise on Boolean chaotic system is two-sided. What is new is that noise may lead to chaos instability, i.e., the noise transforms the original chaotic outputs into periodic outputs, which means the Boolean chaos lacks robustness to noise. Then like other chaotic systems noise contributes to Boolean chaotic unpredictability. The physical origin of noise induced Boolean chaos instability is analyzed that noise may easily change delay time parameter no longer a chaotic parameter in discontinuous chaotic parameter spaces. In addition, simulation and experiments on FPGA show that the increase of the number of nonlinear elements of the chaotic system can improve the Boolean chaos robustness to noise. This research is significant to the design of autonomous Boolean networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the properties of BSDEs in general filtration spaces based on the transposition solutions. We obtain a result that Jensen's inequality holds under the g -expectation and conditional g -expectation in general filtration spaces if and only if g is independent of y and super-homogeneous in z. [ABSTRACT FROM AUTHOR]

In this paper, adaptive synchronization of memristor-based neural networks (MNNs) with time-varying delays is investigated. The dynamical analysis here employs results from the theory of differential equations with discontinuous right-hand sides as introduced by Filippov. Sufficient conditions for the global synchronization of MNNs are established with a general adaptive controller. The update gain of the controller can be adjusted to control the synchronization speed. The obtained results complement and improve the previously known results. Finally, numerical simulations are carried out to demonstrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]