Finite-time Synchronization Of Neural Networks Research Articles

Finite-Time Synchronization of Neural Networks With Proportional Delays for RGB-D Image Protection.

Since the depth information of images facilitates the analysis of the spatial distance of objects in computer vision applications, it is necessary to protect the image depth information. Thus this article proposes a novel red-green-blue-depth (RGB-D) image protection algorithm, which is implemented with the finite-time synchronization (FTS) of neural networks (NNs) with proportional delays via the quantized intermittent control to derive the system synchronization criterion based on Lyapunov stability theory. The performance of RGB-D image protection depends on the synchronization error of the system by driving the system sequence to encrypt the RGB-D image and responding to the system sequence to decrypt the encrypted image. Subsequently, the validity of the proposed criteria is verified by simulation examples, and the practical application of RGB-D image protection is verified.

Finite-Time Synchronization of Neural Networks With Infinite Discrete Time-Varying Delays and Discontinuous Activations.

This article investigates finite-time synchronization of neural networks (NNs) with infinite discrete time-varying delays and discontinuous activations (DDNNs). By virtue of theory of differential inclusions, comparison strategies, and inequality techniques, finite-time synchronization of the underlying DDNNs can be developed via a discontinuous state feedback control law, and the synchronous settling time can be estimated. The delayed state feedback controller and finite-time stability theorem are not employed during the analysis. As a special case, finite-time synchronization of NNs with bounded delays and discontinuous activations is given. Finally, two examples are provided to illustrate the validity of the theories.

Finite-Time Synchronization for Fuzzy Inertial Neural Networks by Maximum Value Approach

In this article, the finite-time synchronization of drive-response fuzzy inertial neural networks with delays is considered. Without applying the finite-time stability theorems and integral inequality approach, by using the maximum value approach of functions and designing two kinds of different controllers of time variable <i>t</i>, two criteria ensuring the finite-time synchronization for the drive-response fuzzy inertial neural networks are put forward. In this article, since the maximum value approach of functions is used and the controllers of time variable <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> are designed in studying the finite-time synchronization, our approach and results on the finite-time synchronization of neural networks are new.

Further study on finite-time synchronization for delayed inertial neural networks via inequality skills

We discuss the finite-time synchronization of a class of delayed inertial neural networks. Getting rid of utilizing some finite-time synchronization theorems and integral inequality skills used in Zhang et al. (2018), by making good use of new inequality skills, two new concise and easily tested criteria to assure the finite-time synchronization for the discussed delayed master-slave inertial neural networks are acquired. The craftsmanship and results of finite-time synchronization for the master-slave neural networks are different from those in some recent works, consequently, our study on the finite-time synchronization of neural networks has definite significance.

Novel Finite-Time Synchronization Criteria for Inertial Neural Networks With Time Delays via Integral Inequality Method.

In this paper, we are concerned with the finite-time synchronization of a class of inertial neural networks with time delays. Without applying some finite-time stability theorems, which are widely applied to studying the finite-time synchronization for neural networks, by constructing two Lyapunov functions and using integral inequality method, two sufficient conditions on the finite-time synchronization for a class of inertial neural networks with time delays are derived. Considering that the method and research results of the finite-time synchronization are different from some existing works, this paper extends the works on the finite-time synchronization of neural networks.

Finite-time Synchronization of Neural Networks with Multiple Proportional Delays via Non-chattering Control

This paper investigates finite-time synchronization of neural networks (NNs) with multiple proportional delays. In order to cope with the difficulties induced by multiple proportional delays, suitable nonlinear variable transformations and new 1-norm-based analytical techniques are developed. By constructing Lyapunov functional and designing new designed controllers, several new sufficient conditions are derived to realize synchronization in finite time. Moreover, estimation of the upper bound of synchronization time is also provided for NNs with bounded delays or proportional delays. The designed controllers without sign function are simple, which means the chattering phenomenon in most of the existing results can be overcome. A numerical simulation is offered to verify the effectiveness of the theoretical analysis.

Finite-time synchronization for delayed complex-valued neural networks via integrating inequality method

Abstract In this paper, we are concerned with the finite-time synchronization for a class of complex-valued neural networks with time delays. Instead of using some finite-time stability theorems which are recently widely applied to investigating the finite-time synchronization for neural networks, by means of using integral inequality method, two novel sufficient conditions on the finite-time synchronization for the above delayed complex-valued neural networks are established. Our results and method on finite-time synchronization for the above neural networks are new and complementary to the existing papers.

Finite-time synchronization of Markovian neural networks with proportional delays and discontinuous activations

In this paper, finite-time synchronization of neural networks (NNs) with discontinuous activation functions (DAFs), Markovian switching, and proportional delays is studied in the framework of Filippov solution. Since proportional delay is unbounded and different from infinite-time distributed delay and classical finite-time analytical techniques are not applicable anymore, new 1-norm analytical techniques are developed. Controllers with and without the sign function are designed to overcome the effects of the uncertainties induced by Filippov solutions and further synchronize the considered NNs in a finite time. By designing new Lyapunov functionals and using M-matrix method, sufficient conditions are derived to guarantee that the considered NNs realize synchronization in a settling time without introducing any free parameters. It is shown that, though the proportional delay can be unbounded, complete synchronization can still be realized, and the settling time can be explicitly estimated. Moreover, it is discovered that controllers with sign function can reduce the control gains, while controllers without the sign function can overcome chattering phenomenon. Finally, numerical simulations are given to show the effectiveness of theoretical results.

Finite-time synchronization for Cohen–Grossberg neural networks with mixed time-delays

Abstract This paper aims to study the finite-time synchronization (i.e., synchronization in finite-time sense) of Cohen–Grossberg neural networks with mixed time delays (both time-varying discrete delay and infinite-time distributed delay). By constructing Lyapunov–Krasovskii functional candidates and using inequality techniques, some new sufficient conditions are derived to design the discontinuous state feedback controllers such that the addressed neural networks can be synchronized in a finite settling time, where the upper bounds of the settling time of synchronization are estimated. The effects of unknown or known time-delay are seriously taken into account, respectively, which lead to two different delay-independent discontinuous state feedback controllers. Thus our results can be applied to the finite-time synchronization of neural networks whether the time delay can be measured or not. As some special cases, our results also improve some recent works. Simulation results show the applicability and the advantages of the proposed finite-time controllers.

Can neural networks with arbitrary delays be finite-timely synchronized?

Finite-time synchronization means the optimality in convergence time, thus many contributions have been made to it in the literature. However, to the best of our knowledge, most of the existing results on finite-time synchronization do not include time-delay. Considering the fact that time-delays especially infinite-time distributed delays are inevitably existing in neural networks, this paper aims to study global synchronization in finite time of neural networks with both time-varying discrete delay and infinite-time distributed delay (mixed delays). The techniques that we apply in this paper are not only different from the techniques employed in existing papers, but also applicable to differential systems with or without delay. Based on new Lyapunov–Krasovskii functional candidate and the new analysis techniques, sufficient conditions guaranteeing the finite-time synchronization of the addressed neural networks are derived by using a class of simple discontinuous state feedback controller. Conditions for realizing finite-time synchronization of neural networks with finite-time distributed delay and without delay are also given. Moreover, estimation of the upper bound of synchronization-time is also provided for neural networks with finite-time distributed delay and without delay. It is shown that the synchronization-time depends on both the initial values and the time-delays of the drive-response systems. Numerical examples demonstrate the effectiveness of the theoretical results.