Synchronization Of Neural Networks Research Articles

Fixed-time synchronization of Inertial Cohen-Grossberg Neural Networks with state dependent delayed impulse control and its application to multi-image encryption

In this paper, we discussed about fixed-time synchronization (FXTS) of Inertial Cohen-Grossberg Neural Networks (ICGNNs) with state-dependent delayed impulses. The Lyapunov stability theory and several useful criteria are utilized to make sure that the control parameters are selected in sync with the intended settling time. Two types of the controller are developed in order to guarantee that error-delayed ICGNNs can be synchronized. A sufficient condition for ensuring FXTS for delayed ICGNNs with desynchronization impulses is investigated. In FXTS, the settling time of ICGNNs will have the smallest upper bound and the settling time of desynchronization will have the largest upper bound. We subsequently conducted numerical simulations to substantiate the validity of the proposed discoveries. Finally, we proposed a multi-image encryption algorithm with the help of ICGNNs and presented the statistical analysis to test its efficacy.

Predefined-Time Synchronization of Multiple Fuzzy Recurrent Neural Networks via a New Scaling Function

Predefined-Time Synchronization of Multiple Fuzzy Recurrent Neural Networks via a New Scaling Function

Event-Based Global Exponential Synchronization for Quaternion-Valued Fuzzy Memristor Neural Networks With Time-Varying Delays

Event-Based Global Exponential Synchronization for Quaternion-Valued Fuzzy Memristor Neural Networks With Time-Varying Delays

Synchronization in scale-free neural networks under electromagnetic radiation.

The functional networks of the human brain exhibit the structural characteristics of a scale-free topology, and these neural networks are exposed to the electromagnetic environment. In this paper, we consider the effects of magnetic induction on synchronous activity in biological neural networks, and the magnetic effect is evaluated by the four-stable discrete memristor. Based on Rulkov neurons, a scale-free neural network model is established. Using the initial value and the strength of magnetic induction as control variables, numerical simulations are carried out. The research reveals that the scale-free neural network exhibits multiple coexisting behaviors, including resting state, period-1 bursting synchronization, asynchrony, and chimera states, which are dependent on the different initial values of the multi-stable discrete memristor. In addition, we observe that the strength of magnetic induction can either enhance or weaken the synchronization in the scale-free neural network when the parameters of Rulkov neurons in the network vary. This investigation is of significant importance in understanding the adaptability of organisms to their environment.

Fixed-Time Aperiodic Intermittent Control for Quasi-Bipartite Synchronization of Competitive Neural Networks.

This paper concerns a class of coupled competitive neural networks, subject to disturbance and discontinuous activation functions. To realize the fixed-time quasi-bipartite synchronization, an aperiodic intermittent controller is initially designed. Subsequently, by combining the fixed-time stability theory and nonsmooth analysis, several criteria are established to ensure the bipartite synchronization in fixed time. Moreover, synchronization error bounds and settling time estimates are provided. Finally, numerical simulations are presented to verify the main results.

Fixed-time synchronization of discontinuous fuzzy competitive neural networks via quantized control

This article concentrates on fixed-time synchronization for discontinuous fuzzy competitive neural networks including time-varying delays. Firstly, to cut down the signal transmission cost, two kinds of nontrivial control strategies, namely quantized control protocols without the linear part, are developed to investigate fixed-time synchronization of discontinuous fuzzy competitive neural networks based on two different types of Lyapunov functions. Then, with the aid of inequality skills and differential inclusion theory, several uncomplicated synchronization criteria are attained and the synchronized time possessing less conservativeness is effectively evaluated. A numerical example is presented at last to testify the validity of theoretical analysis.

Global exponential convergence and synchronization for exponential numerical competitive neural networks with different time scales and fuzzy logic

For nonlocal fuzzy delayed competitive neural networks, a novel difference model is first developed in this article using the Mittag-Leffler Euler difference method. Secondly, the existence of a sole globally bounded solution and globally exponential stability to the discrete-time networks are addressed based on Banach contractive mapping principle and the constant variation formula for sequences. Besides, exponential synchronization of the discrete-time networks is investigated and a feedback controller is designed from the viewpoint of the theory of controls. It is the first time to consider nonlocal fuzzy delayed competitive neural networks by using Mittag-Leffler Euler differences. In contrast to nonlocal discrete approaches, such difference competitive neural networks could preferably figure the features of continuous networks. The difference method in this paper extends and improves the traditional difference approaches, for example, Adams-Bashforth Euler differences. Moreover, this article explores some avenue for the research of discrete-time fractional-order systems and develops a set of theories and methodologies for the future investigations.

Exponential synchronization of nonlinear dynamical systems via an aperiodic hybrid control with actuator saturation and impulse time windows

AbstractConsidering impulse time windows and the actuator saturation, we put forward a novel aperiodic hybrid control scheme to achieve the exponential synchronization of two nonlinear dynamical systems (NDSs). Different from the existing research, the actuator saturation of two types of control signals and impulse time windows are both considered in the exponential synchronization analysis. Some sufficient conditions are found with designing the hybrid controller, and the attraction domain (AD) of the error system is also estimated. On the other hand, the conditions which can ensure the exponential synchronization between two NDSs with this hybrid control without saturation limit are also given. Finally, experimental results for the synchronization of three dimensional Hopfield‐type neural networks and the image reproduction based on the synchronization of neural networks demonstrate the feasibility of our theoretical results.

New method for global exponential synchronization of multi-link memristive neural networks with three kinds of time-varying delays

In this paper, a new direct method based on system solutions is proposed to give global exponential synchronization analysis of multi-link memristive neural networks. The network dynamics are affected by time-varying distribution, leakage and transmission delays, simultaneously. Based on the definition of synchronization, sufficient conditions to ensure the synchronization of multi-link memristive neural networks are investigated, and thereby, a new controller is proposed. Compared with other controllers, the controller design method proposed in this paper is relatively simple, and avoids the construction of Lyapunov–Krasovskii functionals, which greatly reduces the workload. Finally, numerical simulations are given to check the effectiveness of this method.

Projective Synchronization of Inertial Quaternion-Valued Neural Networks via Non-reduced Order Approach

In this article, the issue on projective synchronization of delayed inertial quaternion-valued neural networks (IQVNNs) is investigated. Different from most existing literature, we adopt the non-reduced order approach to deal with IQVNNs described by second order differential equations. By introducing a novel Lyapunov functional, several sufficient criteria are presented in component form to ensure the projective synchronization between master–slave systems. A numerical experiment demonstrates the feasibility of control strategy as well as the correctness of theoretical results.

Fixed-Time Synchronization for Fractional-Order Cellular Inertial Fuzzy Neural Networks with Mixed Time-Varying Delays

Due to the widespread application of neural networks (NNs), and considering the respective advantages of fractional calculus (FC), inertial neural networks (INNs), cellular neural networks (CNNs), and fuzzy neural networks (FNNs), this paper investigates the fixed-time synchronization (FDTS) issues for a particular category of fractional-order cellular-inertial fuzzy neural networks (FCIFNNs) that involve mixed time-varying delays (MTDs), including both discrete and distributed delays. Firstly, we establish an appropriate transformation variable to reformulate FCIFNNs with MTD into a differential first-order system. Then, utilizing the finite-time stability (FETS) theory and Lyapunov functionals (LFs), we establish some new effective criteria for achieving FDTS of the response system (RS) and drive system (DS). Eventually, we offer two numerical examples to display the effectiveness of our proposed synchronization strategies. Moreover, we also demonstrate the benefits of our approach through an application in image encryption.

Synchronization of Fractional Stochastic Neural Networks: An Event Triggered Control Approach

Synchronization of Fractional Stochastic Neural Networks: An Event Triggered Control Approach

Fixed-Time Pinning Common Synchronization and Adaptive Synchronization for Delayed Quaternion-Valued Neural Networks.

This article focuses on the fixed-time pinning common synchronization and adaptive synchronization for quaternion-valued neural networks with time-varying delays. First, to reduce transmission burdens and limit convergence time, a pinning controller which only controls partial nodes directly rather than the entire nodes is proposed based on fixed-time control theory. Then, by Lyapunov function approach and some inequalities techniques, fixed-time common synchronization criterion is established. Second, further to realize the self-regulation function of pinning controller, an adaptive pinning controller which can adjust automatically the control gains is developed, the desired fixed-time adaptive synchronization is achieved for the considered system, and the corresponding criterion is also derived. Finally, the availability of these results is tested by simulation example.

Function projective Mittag-Leffler synchronization of non-identical fractional-order neural networks

This article investigates the function projective Mittag-Leffler synchronization (FPMLS) between non-identical fractional-order neural networks (FONNs). The stability analysis is carried out using an existing lemma for the Lyapunov function in the FONN systems. Based on the stability theorem of FONN, a non-linear controller is designed to achieve FPMLS. Moreover, global Mittag-Leffler synchronization (GMLS) is investigated in the context of other synchronization techniques, such as projective synchronization (PS), anti-synchronization (AS) and complete synchonization (CS). Using the definition of the Caputo derivative, the Mittag-Leffler function and the Lyapunov stability theory, some stability results for the FPMLS scheme for FONN are discussed. Finally, the proposed technique is applied to a numerical example to validate its efficiency and the unwavering quality of the several applied synchronization conditions.

A research study on methods for improving the service quality of a network system based on time information

The network delay is one of the important criteria for selecting computing and networking capabilities. Network delay is one of the important bases of computing network selection. With the continuous development and integration of digital technology, communication technology, and control theory, the research on control theory for networked systems has rapidly advanced. This study proposes an event-triggered mixed time-delay neural network (NN) synchronization method, aiming to reduce network transmission delays through the optimization of NNs, thereby enhancing service responsiveness or reducing errors in data transmission. Firstly, a discrete-driven NN model subject to mixed time delays is established, and the corresponding response network is constructed. In the design of the synchronization controller, a quadratic function is constructed based on the measurement of error states, serving as a threshold for designing the event-trigger mechanism. For the synchronization error system, by constructing a Lyapunov–Krasovskii functional dependent on time delays and a general µ-function, analytical techniques and matrix theory are applied to obtain sufficient criteria for the µ-stability. The validity of the theoretical findings is proven through numerical examples.

Quantized control for predefined-time synchronization of inertial memristive neural networks

Quantized control for predefined-time synchronization of inertial memristive neural networks

Electrophysiological Phenotypes of Hippocampal Synaptic and Network Functions in Cannabinoid Receptor 2 Knockout Mice.

Background: The cannabinoid receptor 2 (CB2R), a cannabinoid receptor primarily expressed in immune cells, has been found in the brain, particularly in the hippocampus, where it plays crucial roles in modulating various neural functions, including synaptic plasticity, neuroprotection, neurogenesis, anxiety and stress responses, and neuroinflammation. Despite this growing understanding, the intricate electrophysiological characteristics of hippocampal neurons in CB2R knockout (CB2R KO) mice remain elusive. Aim and Methods: This study aimed to comprehensively assess the electrophysiological traits of hippocampal synaptic and network functions in CB2R KO mice. The focus was on aspects such as synaptic transmission, short- and long-term synaptic plasticity, and neural network synchrony (theta oscillations). Results: Our findings unveiled multiple functional traits in these CB2R KO mice, notably elevated synaptic transmission in hippocampal CA1 neurons, decreased both synaptic short-term plasticity (paired-pulse facilitation) and long-term potentiation (LTP), and impaired neural network synchronization. Conclusion: In essence, this study yields insightful revelations about the influence of CB2Rs on hippocampal neural functions. By illuminating the electrophysiological modifications in CB2R KO mice, our research enriches the comprehension of CB2R involvement in hippocampal function. Such insights could hold implications for advancing our understanding of the neural mechanisms under the influence of CB2Rs within the brain.

Tradeoff analysis between time cost and energy cost for fixed-time synchronization of discontinuous neural networks

This article focuses on the tradeoff analysis between time and energy costs for fixed-time synchronization (FXTS) of discontinuous neural networks (DNNs) with time-varying delays and mismatched parameters. First, a more comprehensive lemma is systematically established to study fixed-time stability, which is less conservative than those in most current results. Besides, theoretical proof has proven that the upper bounds of the settling time (ST) in this article are more accurate compared to existing results. Second, on the grounds of the new fixed-time stability lemma, fixed-time synchronization problem for discontinuous neural networks with time-varying delays and mismatched parameters is explored, and sufficient conditions for fixed-time synchronization are obtained. Further, the upper bounds of energy cost during the synchronization process are estimated. Third, in order to achieve a balance between time cost and energy cost, the genetic algorithm is utilized to find the satisfactory control parameter. Finally, a numerical example is provided to verify the theoretical analysis’s correctness and the control mechanism’s feasibility.

Delay-independent control for synchronization of memristor-based BAM neural networks with parameter perturbation and strong mismatch via finite-time technology

This paper mainly studies the synchronization problem of memristor-based bidirectional associative memory neural networks (MBAMNNs) via finite-time technology. Different from the existing neural network dynamic models, the given model in this paper is focused on the impact of parameter perturbation and strong mismatch, where strong mismatch includes parameter mismatch and time-varying delay mismatch. These characteristics can make the model be closer to the actual situation. A delay-independent feedback control scheme, which can stabilize the error system within finite-time regardless of whether the past state is known or not, is designed. It is worth noting that the constant is replaced by a function with the exponential term in the delay-independent controller, which can save the control cost to a certain extent. Based on the integral inequality technique, some sufficient conditions for MBAMNNs to converge to the equilibrium point within finite-time are provided. The validity and correctness of the theoretical results are finally confirmed by numerical simulation.

Event-triggered impulsive synchronization of heterogeneous neural networks

Event-triggered impulsive synchronization of heterogeneous neural networks