Synchronization Dynamics Research Articles

Synchronization patterns in a network of diffusively delay-coupled memristive Chialvo neuron map

The inescapable presence of time delay in numerous real-world systems, particularly in neuronal networks, prompts an exploration of its impact on synchronization dynamics. This study employs memristive Chialvo maps to capture the local dynamics of network nodes, while connections are homogeneously described by a linear diffusive function—referred to as electrical couplings in mathematical neuroscience—incorporating a specific time delay. Using Master stability functions (MSFs), analytical assessments are conducted to examine the stability of synchronous solutions, a validation supported by time-averaged synchronization error calculations. This research reveals the time delay's influence on the synchrony, making the synchronous state dependent on the coupling parameter's strength. The behavior of the synchronization manifold is systematically probed across synchronous and asynchronous regions analyzed by the MSF analysis. Lastly, an investigation involving a neuronal network with a global coupling configuration reveals that neurons have a tendency to organize into clusters with distinct time lags.

Dynamic Analysis and Sliding Mode Synchronization Control of Chaotic Systems with Conditional Symmetric Fractional-Order Memristors

In this paper, the characteristics of absolute value memristors are verified through the circuit implementation and construction of a chaotic system with a conditional symmetric fractional-order memristor. The dynamic behavior of fractional-order memristor systems is explored using fractional-order calculus theory and the Adomian Decomposition Method (ADM). Concurrently, the investigation probes into the existence of coexisting symmetric attractors, multiple coexisting bifurcation diagrams, and Lyapunov exponent spectra (LEs) utilizing system parameters as variables. Additionally, the system demonstrates an intriguing phenomenon known as offset boosting, where the embedding of an offset can adjust the position and size of the system’s attractors. To ensure the practical applicability of these findings, a fractional-order sliding mode synchronization control scheme, inspired by integer-order sliding mode theory, is designed. The rationality and feasibility of this scheme are validated through a theoretical analysis and numerical simulation.

Analysis and Synchronization of the Chen System with Fractional Derivative

In this paper, we study the dynamic behaviors and chaos synchronization of the Chen system described by Caputo–Hadamard fractional derivative. First, the existence and uniqueness of a solution to the Chen system with Caputo–Hadamard derivative are proved by qualitative analysis. Further, the stability of equilibria of the considered system is analyzed with the aid of Routh–Hurwitz criteria. Meanwhile, the bifurcation condition of the Caputo–Hadamard Chen system is compared with the integer-order Chen system, where the differences between the two systems are demonstrated numerically. In the study of chaos synchronization of the drive–response Chen systems with Caputo–Hadamard derivative, two control schemes are developed: three nonlinear controllers and single linear controller. The feasibility of two control schemes is verified, and the synchronization performances of these two schemes are compared by numerical simulations. Based on this, the influence of the fractional-order on chaos synchronization performance is illustrated as well.

Synchronization and complex dynamics in locally active threshold memristive neurons with chemical synapses

Synchronization and complex dynamics in locally active threshold memristive neurons with chemical synapses

Dynamic event-triggered synchronization for semi-Markovian switching inertial neural networks with generally uncertain transition rates in finite-time interval

This paper investigates the finite-time synchronization for inertial neural networks with stochastic switching parameters based on dynamic event-triggered protocol. Due to the complexity of network environment, semi-Markovian process is introduced into the modeling of inertial neural networks, in which the transition rates vary with the operating time. The dynamic event-triggered protocol is developed to determine whether the signal is transmitted, in which Zeno phenomenon is eliminated under limited bandwidth resources. The objective is to construct an appropriate dynamic event-triggered control law such that the drive-response system maintains finite-time synchronization under generally uncertain transition rates. Based on the Lyapunov functional theory, finite-time synchronization criterion is proposed for the related inertial neural networks. Furthermore, a dynamic event-triggered controller is constructed in a finite-time interval. A numerical example and an image encryption process are given to show the efficiency of the proposed method.

Synchronization effects in multiplex networks of chaotic maps with memristive interlayer coupling

Models of two-layer multiplex networks of one-dimensional maps with memristive interlayer coupling are studied. In the absence of coupling, the layers exhibit different types of chimera states. Two types of maps are considered as the elements of networks: modified Ricker maps and cubic maps. The effects of complete and partial synchronization of the spatiotemporal dynamics of layers are observed in the case of a homogeneous and inhomogeneous network, respectively. The dependence of the synchronization threshold for the coupling parameter on the initial states of the memristors has been established. This dependence is partially preserved in the case of non-ideal memristors. Diagrams of the synchronization error are constructed on the plane of the initial state variable of memristors and the interlayer coupling coefficient. The effect of noise in memristive coupling elements on synchronization is considered.

Estimation of wind load on supertall buildings using partial output measurements

Supertall buildings possess distinctive characteristics such as high flexibility, low damping, and susceptibility to wind. The impact of the wind load and wind-induced responses plays a critical role in ensuring the safety and functionality of these structures. Consequently, investigating the wind load and wind-induced responses of supertall buildings has immense practical significance and engineering value to guarantee the secure operation of such buildings under strong wind conditions. However, the direct measurement of wind loads and wind-induced responses across all building floors presents substantial challenges, including considerable engineering workload and implementation difficulties. This study proposes a novel approach based on Kalman filtering to estimate wind loads and wind-induced responses in supertall buildings. This approach involves fusing partial acceleration and displacement measurements to estimate wind load and wind-induced response accurately. Additionally, optimized sensor placement was considered based on the iterative process of the effective independence method. Wind-induced responses were obtained through numerical testing and subsequently utilized to estimate the wind loads on a supertall building. Various factors, such as measurement noise, modeling errors of structural dynamic properties, sensor placement, and response synchronization, were thoroughly discussed to assess their impact on the overall analysis.

Landscape fragmentation overturns classical metapopulation thinking

Habitat loss and isolation caused by landscape fragmentation represent a growing threat to global biodiversity. Existing theory suggests that the process will lead to a decline in metapopulation viability. However, since most metapopulation models are restricted to simple networks of discrete habitat patches, the effects of real landscape fragmentation, particularly in stochastic environments, are not well understood. To close this major gap in ecological theory, we developed a spatially explicit, individual-based model applicable to realistic landscape structures, bridging metapopulation ecology and landscape ecology. This model reproduced classical metapopulation dynamics under conventional model assumptions, but on fragmented landscapes, it uncovered general dynamics that are in stark contradiction to the prevailing views in the ecological and conservation literature. Notably, fragmentation can give rise to a series of dualities: a) positive and negative responses to environmental noise, b) relative slowdown and acceleration in density decline, and c) synchronization and desynchronization of local population dynamics. Furthermore, counter to common intuition, species that interact locally ("residents") were often more resilient to fragmentation than long-ranging "migrants." This set of findings signals a need to fundamentally reconsider our approach to ecosystem management in a noisy and fragmented world.

Land use change and coastal water darkening drive synchronous dynamics in phytoplankton and fish phenology on centennial timescales

AbstractAt high latitudes, the suitable window for timing reproductive events is particularly narrow, promoting tight synchrony between trophic levels. Climate change may disrupt this synchrony due to diverging responses to temperature between, for example, the early life stages of higher trophic levels and their food resources. Evidence for this is equivocal, and the role of compensatory mechanisms is poorly understood. Here, we show how a combination of ocean warming and coastal water darkening drive long‐term changes in phytoplankton spring bloom timing in Lofoten Norway, and how spawning time of Northeast Arctic cod responds in synchrony. Spring bloom timing was derived from hydrographical observations dating back to 1936, while cod spawning time was estimated from weekly fisheries catch and roe landing data since 1877. Our results suggest that land use change and freshwater run‐off causing coastal water darkening has gradually delayed the spring bloom up to the late 1980s after which ocean warming has caused it to advance. The cod appear to track phytoplankton dynamics by timing gonadal development and spawning to maximize overlap between offspring hatch date and predicted resource availability. This finding emphasises the importance of land–ocean coupling for coastal ecosystem functioning, and the potential for fish to adapt through phenotypic plasticity.

Synchronization in a higher-order neuronal network with blinking interactions

The synchronization of higher-order networks presents a fascinating area of exploration within nonlinear dynamics and complex networks. Simultaneously, growing research interest focuses on uncovering synchronization dynamics in time-varying networks with time-dependent coupling structures, reflecting their prevalence in real-world systems like neuronal networks. Motivated by this, the present study delves into the synchronization phenomenon within a higher-order network incorporating a blinking coupling scheme. Blinking coupling is an on–off switching coupling that has been demonstrated to enhance synchronization effectively. Its efficacy stems from ensuring synchronization, as the master stability function (MSF) follows a linear pattern. In this study, our objective is to investigate such a time-varying coupling scheme in a higher-order network configuration. We investigate the influence of coupling parameters and blinking frequency on synchronization behavior. Notably, our findings demonstrate that as the blinking frequency increases, the network exhibits a gradual convergence toward the behavior of the average network. Furthermore, leveraging the analytical framework of MSF and the average synchronization error, we provide analytical and numerical evidence confirming that the MSF pattern within the average network transforms into a linear function. The synchronous and asynchronous regions also exhibit a clear separation demarcated by a linear curve across the coupling parameter space. Moreover, our results suggest that incorporating higher-order interactions fosters enhanced synchrony by effectively scaling the synchronization patterns to lower coupling parameter values.

Optimization analysis of distributed energy consumption based on dynamic data synchronization and intelligent control

SummaryWith the rapid development of the global renewable energy source field, the importance of dynamic index processing technology in distributed energy systems has become more and more obvious. To better improve the real‐time dynamic interaction means of microgrids in the energy Internet and optimize the relevant methods for microgrid energy consumption detection, this article proposes to introduce the distributed Hadoop platform into the electrical thermal coupling multivariate data in the form of cluster configuration, and then use the Spark framework to detect and capture real‐time data, to complete the tracking and analysis of energy consumption data. At the same time, the Internet of Things and the cloud intelligent monitoring system are combined to further clean and explore the data, to achieve the in‐depth detection of the energy consumption problem of the microgrid under the premise of reducing the initial investment, and achieve the purpose of reducing the operating cost. In this case, the outliers are detected according to the photovoltaic indicators of photovoltaic power stations, the filtration and purification functions of photovoltaic indicators are used by the nuclear density curve, and the sustainable solar energy is optimized by combining multiple indicators such as wind direction and temperature. Based on reducing energy consumption, the overfitting phenomenon of the controller is controlled, and an optimized controller‐led cloud platform is established. By establishing the objective function model, the robustness of the controller is guaranteed and the detection expectation is satisfied by the experiment of energy consumption data. In addition, when the cloud platform is created, this study uses a genetic algorithm to optimize the controller index and then builds a cloud console detection mechanism that collaborates with the Internet. Through the research, it is found that outliers may lead to the redundancy of energy consumption indicators in the non‐processing state. This study adopts the optimization of energy consumption parameters and the help of a distributed data framework to deal with and effectively solve this problem. In terms of interpolation simulation verification combined with experimental data, this paper proposes to use the Internet of Things, wearable devices, sensors, and other means to monitor the cost of energy consumption, to realize the distributed dynamic storage of massive real‐time data in the process of parallel processing, as well as the evaluation and detection of real‐time data replacement.

Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network

Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing. Collective decision-making with a laser network, employing the chaotic and synchronous dynamics of optically interconnected lasers to address the competitive multi-armed bandit (CMAB) problem, is a highly compelling approach due to its scalability and experimental feasibility. We investigated essential network structures for collective decision-making through quantitative stability analysis. Moreover, we demonstrated the asymmetric preferences of players in the CMAB problem, extending its functionality to more practical applications. Our study highlights the capability and significance of machine learning built upon chaotic lasers and photonic devices.

Synchronization dynamics of phase oscillators on power grid models.

We investigate topological and spectral properties of models of European and US-American power grids and of paradigmatic network models as well as their implications for the synchronization dynamics of phase oscillators with heterogeneous natural frequencies. We employ the complex-valued order parameter-a widely used indicator for phase ordering-to assess the synchronization dynamics and observe the order parameter to exhibit either constant or periodic or non-periodic, possibly chaotic temporal evolutions for a given coupling strength but depending on initial conditions and the systems' disorder. Interestingly, both topological and spectral characteristics of the power grids point to a diminished capability of these networks to support a temporarily stable synchronization dynamics. We find non-trivial commonalities between the synchronization dynamics of oscillators on seemingly opposing topologies.

Testing scale-dependent temporal and spatial biases in relative chronology using AMS 14C dating: A case study of Early–Middle Cucuteni-Tripolye sites in Southeastern Europe

Aiming at developing a syncretic chronology for Eneolithic sites in Romania, Moldova and Ukraine, this paper tests the hypothesis that relative material culture sequences will be preserved within lower-order taxonomic units and collapse within higher-order units. We examine relative sequences for sub-groups of the Cucuteni-Tripolye cultural complex (ca. 5050–2950 BCE) using Bayesian sequencing of extant and newly obtained radiocarbon dates. The traditional archaeological perspective holds that the space-time expansion of this cultural complex occurred in rapidly disseminated horizons, with little cross-regional variability in dating. Absolute dating efforts from the 1960s–2000s did little to clarify the situation, especially in the upper section of the Dniester river valley in Ukraine, where numerous older radiometric 14C dates present an improbably early chronology for sites of the Early and early Middle Tripolye periods (Tripolye A and BI). Here we present 21 new AMS 14C dates from early settlements in Southwestern Ukraine and Moldova, with results dating to the latter half of the fifth millennium and the beginning of the fourth millennium BCE. Eschewing the concept of monolithic horizons, we compile both old and new data into regionally distinct sequences, enabling a more nuanced examination of material synchronization and cross-regional settlement dynamics.

Synchronization analyze of k-uniform hyper-networks

Hyper-networks tend to perform better in representing multivariate relationships among nodes. Yet, due to the complexity of the hyper-network structure, research in synchronization dynamics is rarely involved. In this paper, a Kuramoto model more suitable for k-uniform hyper-networks is proposed. And the generalized Laplacian matrix expression of the k-uniform hyper-network is present. We use the eigenvalue ratio of the generalized Laplacian matrix to quantify synchronization. And we studied the effects of some important structure parameters on the synchronization of three types of k-uniform hyper-networks. And obtained different relationships between synchronization and these parameters. The results show the synchronization of the k-uniform hyper-networks is related to both structure and parameters. And as the size of the nodes increases, the synchronization ability gradually increases for ER random hyper-network, while that gradually decreases for NW small-world hyper-network and BA scale-free hyper-network. As the uniformity increases, the synchronization ability of all three types of uniform hyper-networks increases. In addition, when the structure and node size are fixed, the synchronization ability increases with the increase of the hyper-clustering coefficient in BA scale-free hyper-network and ER random hyper-network, while it decreases with the increase of the hyper-clustering coefficient in NW small-world hyper-network.

Trajectory Generation Method for Serial Robots in Hybrid Space Operations

The hybrid space of robots is divided into task space and joint space, with task space focused on trajectory-tracking accuracy, while joint space considers dynamic responsiveness and synchronization. Therefore, the robot-motion control systems need to effectively integrate both aspects, ensuring precision in task trajectory while promptly responding to unforeseen environmental events. Hence, this paper proposes an online trajectory-generation method for robots in both joint and task spaces. In task space, a planning approach is presented for high-precision NURBS curves. The global NURBS curve is segmented into several rational Bezier curves, establishing local coordinate systems for control points. This ensures that all local control points meet the chord error constraint, guaranteeing trajectory accuracy. To address the feed rate dynamic planning issue for segmented curves, an improved online S-shape feed-rate scheduling framework is introduced. This framework dynamically adjusts the current execution speed to meet task requirements. In joint space, an offline velocity planning based on a time synchronization scheme and a multi-dimensional synchronization technique based on the principle of spatial-coordinate system projection are proposed. Building upon the offline scheme, it allows for the modification of the target state for any sub-dimension during the motion process, with the remaining dimensions adapting accordingly. Simulation and experimentation demonstrate that the two proposed online trajectory generations for robot motion spaces, while ensuring task trajectory accuracy, effectively handle external unexpected events. They ensure joint synchronization and smoothness, carrying significant practical implications and application value for the stability of robot systems.

Observer‐based hybrid triggered control design for cluster synchronization of nonlinear complex dynamical networks

SummaryThe issues of cluster synchronization and state estimation for a class of nonlinear complex dynamical networks are concurrently focused in this study. In precise, led by the design of complex dynamical networks, a proportional integral‐based observer is implemented for achieving robustness, by the virtue of which the states of examined networks are estimated. Thereupon, a proportional integral observer‐based hybrid triggered control scheme is proposed, wherein, the switch‐over between time‐ and event‐ triggered scheme is governed through a Bernoulli distributed random variable. Further, with the application of mixed and passivity performance technique, the external disturbances for the addressed complex dynamical networks are reduced. Based on the Lyapunov stability theory, the requisite sufficient conditions in respect of linear matrix inequalities are provided which ensures the discussed complex dynamical network's synchronization phenomenon. In a further, the presented criteria are exploited for the attainment of required controller, proportional and integral gain matrices. In conclusion, the applicability of proposed results and efficacy of the undertaken strategy are evidently proven with the illustration of two numerical examples with Chua's circuit and Goodwin oscillator model.

An Improved Strategy of Grid-Forming DFIG Based on Disturbance Rejection Stator Flux Control

Grid-forming (GFM) control are promising for doubly fed induction generator-based wind turbines (DFIG -Based WTs) due to its grid-support ability and stable operation when connected to weak grid. However, GFM-DFIG might suffer from instability under symmetrical grid fault, if its electromagnetic and synchronous dynamics are poor. Therefore, an improved strategy of GFM-DFIG based on disturbance rejection stator flux control (DRSFC) is proposed in this paper. The DRSFC consists of a PI-based rotor-current loop and an active disturbance rejection control (ADRC)-based stator-flux outer loop, and both the better electromagnetic performance and the steady-state voltage source operation are achieved. Besides, impacts of DRSFC on GFM-DFIG are exposed via small-signal and large-signal analysis, and the potential inertia in the inner control loops is revealed. It is suggested that this potential inertia is reshaped by DRSFC, so the impact of inner control loop on the synchronization of GFM-DFIG can be quantitatively studied and controlled. On this basis, a pair of asymmetrical compensation are designed to improve its electromagnetic decay and synchronous dynamic. Finally, experiments are carried out to verify the effectiveness of proposed strategy.

Marine magnetotelluric and controlled source electromagnetics signal simulative generator

Ocean bottom electromagnetic receivers (OBEMs) are invaluable tools for observing seafloor electromagnetic signals, primarily used in magnetotelluric (MT) sounding and marine controlled source electromagnetics (MCSEM). However, the high operational costs of deploying OBEMs require thorough pre-deployment testing to enhance the data reliability and quality. Commercial signal generators used for MT and controlled source electromagnetic (CSEM) testing often fall short of the restricted terms of the frequency range, signal-to-noise ratio, storage depth, time synchronization, and amplitude dynamics. To address these limitations, we developed a specialized marine MT and CSEM signal simulative generator. The generator employs pseudorandom number code-based white noise to simulate MT signals and uses multi-frequency synthesis and amplitude modulation (AM) to generate CSEM signals. The system is developed using a low-power microcontroller unit and a field programmable gate array platform, integrating various circuits such as power, clock, encoding, control, data storage, chopper, and amplification circuits. The generator simultaneously outputs four-channel white noise and AM signals. The white noise feature provides a wide and adjustable frequency band and amplitude, while the AM feature provides a large dynamic amplitude range and multi-frequency synthesis at various carrier frequency points. Comprehensive testing verified the performance of the proposed simulative generator. The test results demonstrate that the MT simulated signals cover a frequency range from 1 E4s to 100 Hz, providing adjustable amplitudes between 10 μVpp and 10 mVpp. The CSEM simulated signals include standard square waves, third harmonic waves, and seventh harmonic waves, with amplitudes adjustable from 10 mVpp to 10 Vpp. The proposed simulative generator enhances the efficiency of OBEM functional testing and provides essential equipment support for maintaining the quality of marine data.

Physical-layer key distribution using synchronous complex dynamics of DBR semiconductor lasers

Common-signal-induced synchronization of semiconductor lasers with optical feedback inspired a promising physical-layer key distribution with information-theoretic security and potential in high rate. A significant challenge is the requirement to shorten the synchronization recovery time for increasing the key rate without sacrificing the operation parameter space for security. Here, open-loop synchronization of wavelength-tunable multi-section distributed Bragg reflector lasers is proposed as a solution for physical-layer key distribution. Experiments show that the synchronization is sensitive to two operation parameters, i.e., currents of grating section and phase section. Furthermore, fast wavelength-shift keying synchronization can be achieved by direct modulation on one of the two currents. The synchronization recovery time is shortened by one order of magnitude compared to close-loop synchronization. An experimental implementation is demonstrated with a final key rate of 5.98 Mbit/s over 160 km optical fiber distance. It is thus believed that fast-tunable multi-section semiconductor lasers open a new avenue for a high-rate physical-layer key distribution using laser synchronization.