Synchronization Of Networks Research Articles

Finite-Time Adaptive Synchronization and Fixed-Time Synchronization of Fractional-Order Memristive Cellular Neural Networks with Time-Varying Delays

Asymptotic synchronization requires continuous external control of the system, which is unrealistic considering the cost of control. Adaptive control methods have strong robustness to uncertainties such as disturbances and unknowns. On the other hand, for finite-time synchronization, if the initial value of the system is unknown, the synchronization time of the finite-time synchronization cannot be estimated. This paper explores the finite-time adaptive synchronization (FTAS) and fixed-time synchronization (FDTS) of fractional-order memristive cellular neural networks (FMCNNs) with time-varying delays (TVD). Utilizing the properties and principles of fractional order, we introduce a novel lemma. Based on this lemma and various analysis techniques, we establish new criteria to guarantee FTAS and FDTS of FMCNNs with TVD through the implementation of a delay-dependent feedback controller and fractional-order adaptive controller. Additionally, we estimate the upper bound of the synchronization setting time. Finally, numerical simulations are conducted to confirm the validity of the finite-time and fixed-time stability theorems.

On fixed-time interlayer synchronization of two-layer multiweighted complex dynamic networks: An economic and practical non-chattering adaptive control approach

This paper investigates the adaptive fixed-time (FxT) interlayer synchronization issue of two-layer complex dynamic networks (CDNs), where both multiple weights and interlayer links are considered. Above all, a new FxT stability lemma is given, which offers a more flexible condition and some more precise estimates of stabilized convergence time compared with the existing results. Subsequently, an economic and practical non-chattering continuous adaptive controller is designed, which is superior to the currently available ones by excluding the signum function and linear part and removing the parity restriction on the exponents in control law. Furthermore, by means of the Lyapunov method and the newly-established FxT stability theory, some novel FxT interlayer synchronization criteria are derived for the addressed two-layer multiweighted CDNs with the designed non-chattering continuous adaptive controller, and the synchronized settling time (SST) is also reckoned. It is found that the interlayer links weights can contribute to the interlayer synchronization, and the SST becomes smaller with the increase of interlayer link weight. Finally, the proposed results are validated by numerical examples.

Pinning intra-layer synchronization in multiplex networks of nonidentical layers

In this paper, we investigate intra-layer synchronization in multiplex networks via pinning control strategies. All layers have different topologies and different intrinsic node dynamics. Novel sufficient criteria are derived for pinning intra-layer synchronization of multiplex networks, which indicates that the pinned node set can stabilize the multiplex network if, for each layer, the pinned node(s) can access all unpinned nodes. A distributed adaptive algorithm and finite-time pinning algorithm for intra-layer synchronization of multiplex networks are also proposed. Finally, we give some numerical examples to validate the effectiveness of these theoretical results.

Multi-feature Learning Adaptive Network for Underwater Image Enhancement

Underwater image enhancement faces variety of challenges owing to the diversity of underwater scenes (viewed as water types) and the rich multi-frequency information. To deal with these challenges, this paper proposes a multi-feature learning adaptive underwater image enhancement network comprising an adaptive module and a dual-layer synchronous enhancement network. First, we design an adaptive module which enables the determination of water type inside the model and eliminates the negative effect of water type diversity by building water type related features. Then, the model learns high-frequency and low-frequency features through a dual-layer synchronous enhancement network to extract more comprehensive information. Finally, the outputs of the dual-layer network are merged to obtain more realistic underwater enhanced images. Numerous experiments have shown that the proposed method outperforms the comparison method for visual perception and assessment metrics.

Finite-time synchronisation for Markovian master-slave neural networks with weighted try-once-discard protocol and static intermittent control

ABSTRACT This paper studies the finite-time synchronisation (FTS) issue for Markovian master-slave neural networks (NNs). A weighted try-once-discard (WTOD) protocol is introduced to overcome communication constraints, and an intermittent control strategy is employed to surmount energy constraints. A WTOD and parameter dependent static feedback controller is designed for the slave neural networks. Then, sufficient conditions are developed to achieve FTS for the considered master-slave NNs, and the controller design method is obtained. Finally, a numerical example is provided to demonstrate and verify the derived results.

Stochastic synchronization of Kuramoto-oscillator network with pinning control

The issue of time cost for the Kuramoto-oscillator network synchronization has received widespread attention. However, there is no relevant research on the stochastic synchronization of the Kuramoto-oscillator network with partial uncontrollable oscillators. This article investigates the synchronization problem of the Kuramoto-oscillator network in noisy environments using the pinning control strategy and multilayer distributed control. Previous research requires control of all oscillators, which implies a great control cost. To reduce the control cost, the finite/fixed time pinning controllers are designed to provide the conditions for synchronization in noisy environments, and then the upper bounds on the convergence time of the network are estimated. Finally, numerical simulations are performed to justify the theoretical conclusions.

Synchronization of Stochastic Neural Networks Using Looped-Lyapunov Functional and Its Application to Secure Communication.

This study aims to investigate the synchronization of user-controlled and uncontrolled neural networks (NNs) that exhibit chaotic solutions. The idea behind focusing on synchronization problems is to design the user-desired NNs by emulating the dynamical properties of traditional NNs rather than redefining them. Besides, instead of conventional NNs, this study considers NNs with significant factors such as time-dependent delays and uncertainties in the neural coefficients. In addition, information transmission over transmission may experience stochastic disturbances and network transmission. These factors will result in a stochastic differential NN model. Analyzing the NNs without these factors may be incompatible during the implementation. Theoretically, the model with stochastic disturbances can be considered a stochastic differential model, and the stability conditions are derived by employing Itô's formula and appropriate integral inequalities. To achieve synchronization, the sampled-data-based control scheme is proposed because it is more effective while information is being transmitted over networks. In contrast to the existing studies, this study contributes in terms of handling stochastic disturbances, effects of time-varying delays, and uncertainties in the system parameters via looped-type Lyapunov functional. Besides this, in the application view, delayed NNs are employed as a cryptosystem that helps to secure the transmission between the sender and the receiver, which is explored by illustrating the statistical measures evaluated for the standard images. From the simulation results, the proposed control and derived sufficient conditions can provide better synchronization and the proposed delayed NNs give a better cryptosystem.

Fixed-Time Synchronization of Impulsive Octonion-Valued Fuzzy Inertial Neural Networks via Improving Fixed-Time Stability

Fixed-Time Synchronization of Impulsive Octonion-Valued Fuzzy Inertial Neural Networks via Improving Fixed-Time Stability

Message-Passing-Based Distributed Cooperative Simultaneous Localization and Synchronization in Dynamic Asynchronous Networks

Message-Passing-Based Distributed Cooperative Simultaneous Localization and Synchronization in Dynamic Asynchronous Networks

Complete synchronization of three-layer Rulkov neuron network coupled by electrical and chemical synapses.

This paper analyzes the complete synchronization of a three-layer Rulkov neuron network model connected by electrical synapses in the same layers and chemical synapses between adjacent layers. The outer coupling matrix of the network is not Laplacian as in linear coupling networks. We develop the master stability function method, in which the invariant manifold of the master stability equations (MSEs) does not correspond to the zero eigenvalues of the connection matrix. After giving the existence conditions of the synchronization manifold about the nonlinear chemical coupling, we investigate the dynamics of the synchronization manifold, which will be identical to that of a synchronous network by fixing the same parameters and initial values. The waveforms show that the transient chaotic windows and the transient approximate periodic windows with increased or decreased periods occur alternatively before asymptotic behaviors. Furthermore, the Lyapunov exponents of the MSEs indicate that the network with a periodic synchronization manifold can achieve complete synchronization, while the network with a chaotic synchronization manifold can not. Finally, we simulate the effects of small perturbations on the asymptotic regimes and the evolution routes for the synchronous periodic and the non-synchronous chaotic network.

Synchronization of Drive-Response Networks with Delays on Time Scales

Synchronization of Drive-Response Networks with Delays on Time Scales

Sampled-Data Control for Exponential Synchronization of Delayed Inertial Neural Networks With Aperiodic Sampling and State Quantization.

This article is devoted to dealing with exponential synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) under the framework of aperiodic sampling and state quantization. First, by taking the effect of aperiodic sampling and state quantization into consideration, a novel quantized sampled-data (QSD) controller with time-varying control gain is designed to tackle the exponential synchronization of INNs. Second, considering the available information of the lower and upper bounds of each HTVD, a refined Lyapunov-Krasovskii functional (LKF) is proposed. Meanwhile, an improved looped-functional method is utilized to fully capture the characteristic of practical sampling patterns and further relax the positive definiteness requirement for LKF. Consequently, less conservative exponential synchronization conditions with extra flexibility are derived. Finally, a numerical example is employed to demonstrate the effectiveness and advantages of the proposed synchronization method.

A Synchronous Training Hypergraph Neural Network for Power Allocation in Multi-Cell Multi-User Networks

A Synchronous Training Hypergraph Neural Network for Power Allocation in Multi-Cell Multi-User Networks

Finite-Time pth Moment Asymptotically Bounded for Stochastic Nonlinear Systems and Its Application in Neural Networks Synchronization

Finite-Time <i>p</i>th Moment Asymptotically Bounded for Stochastic Nonlinear Systems and Its Application in Neural Networks Synchronization

Microdosing psychedelics in the treatment of ADHD and comorbid disorders

AbstractMicrodosing psychedelics has garnered considerable attention within both nonprofessional circles and the scientific community in recent years. This method involves taking small, non-hallucinogenic doses of substances like LSD or psilocybin over weeks or months, purportedly to enhance specific behaviors, emotions, or address psychiatric conditions.Exploring these assertions is crucial given the potential therapeutic value of microdosing, especially in conditions that respond positively to full psychedelic doses, such as depression. The full psychedelic experience might not always be suitable due to various factors like age, capacity to consent or comprehend the experience (e.g., dementia), or individual personality traits that might hinder surrendering to the experience. Microdosing could potentially serve as a maintenance therapy post-full dose administration, aiding specific psychological or biological processes during therapy or therapeutic exercises.Recent studies in healthy individuals highlight that small psychedelic doses have nuanced effects on pain perception, mood, neuroplasticity, sleep duration, brain connectivity, and default mode network synchronicity. However, some parameters show null effects after both single and repeated administration. Our survey research uncovered that individuals with ADHD reported symptom relief through microdosing, deeming it more effective than their conventional treatments. Subsequently, we conducted a naturalistic study following individuals with ADHD across a 4-week microdosing period. Our findings indicated a reduction in symptoms over time, an increase in trait mindfulness, and a decrease in neuroticism compared to baseline. While these results are intriguing, they necessitate validation in a clinical trial. We have recently concluded such a trial and are currently analyzing the data to further explore these effects.Disclosure of InterestK. Kuypers Grant / Research support from: The author is a principal investigator on a research project that is sponsored by Mindmed, a company that is developing psychedelic medicines.

Microwave frequency transfer over 3000-km fiber based on optical frequency combs and active noise cancellation

Time and frequency transfer plays an important role in the fields of navigation and positioning, remote sensing, and fundamental physics. The performance of long-haul frequency transfer is ultimately limited by accumulated phase and amplitude noise in fiber propagation. In this work we overcome these limitations and demonstrate a frequency transfer system over 3000 km of indoor spooled fibers via repetition-frequency-locked frequency combs, which benefit from the extremely high signal-to-noise ratio. With the help of the digital phase discrimination and compensation method and active power stabilization, the dramatic phase and amplitude variations are sufficiently suppressed, respectively, which results in the residual instability 7.5×10−14 at 1 s and 1.0×10−16 at 10 000 s, without optical-electrical-optical recovery by the regeneration system. Our results mark a breakthrough in building large-scale, high-precision synchronization networks. Published by the American Physical Society 2024

PDASTSGAT: An STSGAT-Based Multipath Data Scheduling Algorithm

How to select the transmitting path in MPTCP scheduling is an important but open problem. This paper proposes an intelligent data scheduling algorithm using spatiotemporal synchronous graph attention neural networks to improve MPTCP scheduling. By exploiting the spatiotemporal correlations in the data transmission process and incorporating graph self-attention mechanisms, the algorithm can quickly select the optimal transmission path and ensure fairness among similar links. Through simulations in NS3, the algorithm achieves a throughput gain of 7.9% compared to the PDAA3C algorithm and demonstrates improved packet transmission performance.

Функциональная модель объединенного источника комплексной системы синхронизации и доставки шкалы времени для крупной и распределенной системы технологического назначения

Purpose: to develop scientific and technical proposals and requirements for building sources of synchrosignals and messages of a single time for the integrated system of synchronization and delivery of the time scale depending on the requirements in frequency-time support signals and the current communication chain architecture in the framework of solving a unified synchronization system in phase time and the frequency in large territorially distributed and industrial technological systems. Methods: the applied research methods include the fundamental provisions of the theory of constructing synchronization systems, as well as infocommunication systems and networks. Results: a functional model of a united source of the integrated system of synchronization and delivery of the time scale, which includes a complete set of elements to perform the functions of synchronization and delivery of single-time signals in existing and promising complex heterogeneous networks with various architecture and heterogeneous equipment that differs from the existing fully taking into account the existing ones in Currently, technologies for delivery of single time and frequency signals, both on the basis of switching packages and on the basis of switching of channels. The proposed functional model of the united source of the integrated system of synchronization and delivery of the time scale will allow to reasonably approach the design of the means of transmitting and delivering single time and frequency signals to various and heterogeneous communication networks, taking into account all the needs for frequency-time support signals and limiting existing communication networks. Practical significance: the use of sources of single-time and frequency signals, which are based on the proposed functional model, will ensure a flexible infrastructure to ensure the unity of the unity of the unified time and clock network synchronization necessary for the correct functioning of communication equipment; Satisfy the requirements of synchronization signals and uniform messages for promising and existing consumers as much as possible; ensure the possibility of a phased transition of the construction of communication networks from the technologies of the channels to the technology of switching packages due to flexibility in the use of routes and transmission systems necessary for the spread of synchrosignals and uniform messages.

Clustering Component Synchronization of Nonlinearly Coupled Complex Networks via Pinning Control

In this paper, the problem of clustering component synchronization of nonlinearly coupled complex networks with nonidentical nodes and asymmetric couplings is investigated. A pinning control strategy is designed to achieve the clustering component synchronization with respect to the specified components. Based on matrix analysis and stability theory, clustering component synchronization criteria are established. Two numerical simulations are also provided to show the effectiveness of the theoretical results.

Variant-specific invitro neuronal network phenotypes and drug sensitivity in SCN2A developmental and epileptic encephalopathy.

De novo variants in the NaV1.2 voltage-gated sodium channel gene SCN2A are among the major causes of developmental and epileptic encephalopathies (DEE). Based on their biophysical impact on channel conductance and gating, SCN2A DEE variants can be classified into gain-of-function (GoF) or loss-of-function (LoF). Clinical and functional data have linked early seizure onset DEE to the GoF SCN2A variants, whereas late seizure onset DEE is associated with the loss of SCN2A function. This study aims to assess the impact of GoF and LoF SCN2A variants on cultured neuronal network activity and explore their modulation by selected antiseizure medications (ASM). To this end, primary cortical cultures were generated from two knock-in mouse lines carrying variants corresponding to human GoF SCN2A p.R1882Q and LoF p.R853Q DEE variant. Invitro neuronal network activity and responses to ASM were analyzed using multielectrode array (MEA) between 2 and 4 weeks in culture. The SCN2A p.R1882Q neuronal cultures showed significantly greater mean firing and burst firing. Their network synchronicity was also higher. In contrast, the SCN2A p.R853Q cultures showed lower mean firing rate, and burst firing events were less frequent. The network synchronicity was also lower. Phenytoin and levetiracetam reduced the excitability of GoF cultures, while retigabine showed differential and potentially beneficial effects on cultures with both GoF and LoF variants. We conclude that invitro neuronal networks harboring SCN2A GoF or LoF DEE variants present with distinctive phenotypes and responses to ASM.