Search Results (38)

We investigate the stochastic disruption of synchronization patterns in a system of two non-identical Rulkov neurons coupled via an electrical synapse. By analyzing the system deterministic dynamics, we identify regions of mono-, bi-, and tristability, corresponding to distinct synchronization regimes as a function of coupling strength. Introducing stochastic perturbations to the coupling parameter, we explore how noise influences synchronization patterns, leading to transitions between different regimes. Notably, we find that increasing noise intensity disrupts lag synchronization, resulting in intermittent switching between a synchronous three-cycle regime and asynchronous chaotic states. This intermittency is closely linked to the structure of chaotic transient basins, and we determine a noise intensity range in which such behavior persists, depending on the coupling strength. Using both numerical simulations and an analytical confidence ellipse method, we provide a comprehensive characterization of these noise-induced effects. Our findings contribute to the understanding of stochastic synchronization phenomena in coupled neuronal systems and offer potential implications for neural dynamics in biological and artificial networks. Full article

The increase in installed PV capacity worldwide and the intermittent nature of solar resources highlight the importance of power prediction for grid integration of this technology. Therefore, there is an urgent need for an effective prediction model, but the choice of model hyperparameters greatly affects the prediction performance. In this paper, a multi-strategy improved snowmelt algorithm (MISAO) is proposed for optimizing intrinsic computing-expressive empirical mode decomposition with adaptive noise (ICEEMDAN) and weighted least squares support vector machine for PV power forecasting. Firstly, a cyclic chaotic mapping initialization strategy is used to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain quickly. Secondly, the Gaussian diffusion strategy enhances the local exploration ability of the intelligences and extends their search in the solution space, effectively preventing them from falling into local optima. Finally, a stochastic follower search strategy is employed to reserve better candidate solutions for the next iteration, thus achieving a robust exploration–exploitation balance. With these strategies, the optimization performance of MISAO is comprehensively improved. In order to comprehensively evaluate the optimization performance of MISAO, a series of numerical optimization experiments were conducted using IEEE CEC2017 and test sets, and the effectiveness of each improvement strategy was verified. In terms of solution accuracy, convergence speed, robustness, and scalability, MISAO was compared with the basic SAO, various state-of-the-art optimizers, and some recently developed improved algorithms. The results showed that the overall optimization performance of MISAO is excellent, with Friedman average rankings of 1.80 and 1.82 in the two comparison experiments. In most of the test cases, MISAO delivered more accurate and reliable solutions than its competitors. In addition, the altered algorithm was applied to the selection of hyperparameters for the ICEEMDAN-WLSSVM PV prediction model, and seven neural network models, including WLSSVM, ICEEMDAN-WLSSVM, and MISAO-ICEEMDAN-WLSSVM, were used to predict the PV power under three different weather types. The results showed that the models have high prediction accuracy and stability. The MAPE, MAE and RMSE of the proposed model were reduced by at least 25.3%, 17.8% and 13.3%, respectively. This method is useful for predicting the output power, which is conducive to the economic dispatch of the grid and the stable operation of the power system. Full article

Josephson junctions (JJs) are superconductor-based devices used to build highly sensitive magnetic flux sensors called superconducting quantum interference devices (SQUIDs). These sensors may vary in design, being the radio frequency (RF) SQUID, direct current (DC) SQUID, and hybrid, such as D-SQUID. In addition, recently many of JJ’s applications were found in spiking models of neurons exhibiting nearly biological behavior. In this study, we propose and investigate a new circuit model of a sensory neuron based on DC SQUID as part of the circuit. The dependence of the dynamics of the designed model on the external magnetic flux is demonstrated. The design of the circuit and derivation of the corresponding differential equations that describe the dynamics of the system are given. Numerical simulation is used for experimental evaluation. The experimental results confirm the applicability and good performance of the proposed magnetic-flux-sensitive neuron concept: the considered device can encode the magnetic flux in the form of neuronal dynamics with the linear section. Furthermore, some complex behavior was discovered in the model, namely the intermittent chaotic spiking and plateau bursting. The proposed design can be efficiently applied to developing the interfaces between circuitry and spiking neural networks. However, it should be noted that the proposed neuron design shares the main limitation of all the superconductor-based technologies, i.e., the need for a cryogenic and shielding system. Full article

The insect predator–prey system mediates several feedback mechanisms which regulate species abundance and spatial distribution. However, the spatiotemporal dynamics of such discrete systems with the refuge effect remain elusive. In this study, we analyzed a discrete Holling type II model incorporating the refuge effect using theoretical calculations and numerical simulations, and selected moths with high and low growth rates as two exemplifications. The result indicates that only the flip bifurcation opens the routes to chaos, and the system undergoes four spatiotemporally behavioral patterns (from the frozen random pattern to the defect chaotic diffusion pattern, then the competition intermittency pattern, and finally to the fully developed turbulence pattern). Furthermore, as the refuge effect increases, moths with relatively slower growth rates tend to maintain stability at relatively low densities, whereas moths with relatively faster growth rates can induce chaos and unpredictability on the population. According to the theoretical guidance of this study, the refuge effect can be adjusted to control pest populations effectively, which provides a new theoretical perspective and is a feasible tool for protecting crops. Full article

Considering that wind power is proportional to the cube of the wind speed variable, which is highly random, complex power grid management tasks have arisen as a result. Wind speed prediction in the short term is crucial for load dispatch planning and load increment/decrement decisions. The chaotic intermittency of speed is often characterised by inherent linear and nonlinear patterns, as well as nonstationary behaviour; thus, it is generally difficult to predict it accurately and efficiently using a single linear or nonlinear model. In this study, wavelet transform (WT), autoregressive integrated moving average (ARIMA), extreme gradient boosting trees (XGBoost), and support vector regression (SVR) are combined to predict high-resolution short-term wind speeds obtained from three Southern African Universities Radiometric Network (SAURAN) stations: Richtersveld (RVD); Central University of Technology (CUT); and University of Pretoria (UPR). This hybrid model is termed WT-ARIMA-XGBoost-SVR. In the proposed hybrid, the ARIMA component is employed to capture linearity, while XGBoost captures nonlinearity using the wavelet decomposed subseries from the residuals as input features. Finally, the SVR model reconciles linear and nonlinear predictions. We evaluated the WT-ARIMA-XGBoost-SVR’s efficacy against ARIMA and two other hybrid models that substitute XGBoost with a light gradient boosting machine (LGB) component to form a WT-ARIMA-LGB-SVR hybrid model and a stochastic gradient boosting machine (SGB) to form a WT-ARIMA-SGB-SVR hybrid model. Based on mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R²), and prediction interval normalised average width (PINAW), the proposed hybrid model provided more accurate and reliable predictions with less uncertainty for all three datasets. This study is critical for improving wind speed prediction reliability to ensure the development of effective wind power management strategies. Full article

This paper primarily focuses on the chaos synchronisation analysis of neural networks (NNs) under a hybrid controller. Firstly, we design a suitable hybrid controller with saturated impulse control, combined with time-dependent intermittent control. Both controls are low-energy consumption and discrete, aligning well with industrial development needs. Secondly, the saturation function in the chaotic neural network is addressed using the polyhedral representation method and the sector nonlinearity method, respectively. By integrating the Lyapunov stability theory, Jensen’s inequality, the mathematical induction method, and the inequality reduction technique, we establish suitable time-dependent Lyapunov generalised equations. This leads to the estimation of the domain of attraction and the derivation of local exponential stability conditions for the error system. The validity of the achieved theoretical criteria is eventually demonstrated through numerical experiment simulations. Full article

Recent studies have indicated that terahertz time-domain spectroscopy (THz-TDS) can stably and efficiently acquire output spectra using an affordable and compact multimode laser diode (MMLD) with delayed optical feedback as the light source. This research focused on a numerical analysis of the optimal conditions for employing an MMLD with delayed optical feedback (a chaotic oscillating laser diode) in THz-TDS utilizing multimode rate equations. The findings revealed that the intermittent chaotic output generated by the MMLD, characterized by concurrent picosecond pulse oscillations lasting several tens of picoseconds, proved to be highly effective for THz-TDS. By appropriately setting the amounts for the injection current and optical feedback and the delay time for the optical feedback, intermittent chaotic oscillation could be attained within a considerably broad parameter range. The generation of intermittent chaotic oscillations was confirmed by observing their characteristic asymmetric spectral shapes. Moreover, both the MMLD output spectrum and the THz-TDS output spectrum exhibited consistently stable shapes at the microsecond scale, demonstrating the attractor properties inherent in an MMLD with delayed optical feedback. Full article

The traditional theory of chaotic intermittency developed for return maps hypothesizes a uniform density of reinjected points from the chaotic zone to the laminar one. In the past few years, we have described how the reinjection probability density function (RPD) can be generalized as a power law function. Here, we introduce a broad and general analytical approach to determine the RPD function and other statistical variables, such as the characteristic relation traditionally utilized to characterize the chaotic intermittency type. The proposed theoretical methodology is simple to implement and includes previous studies as particular cases. It is compared with numerical data, the M function methodology, and the Perron–Frobenius technique, showing high accuracy between them. Full article

Chaotic intermittency is characterized by a signal that alternates aleatory between long regular (pseudo-laminar) phases and irregular bursts (pseudo-turbulent or chaotic phases). This phenomenon has been found in physics, chemistry, engineering, medicine, neuroscience, economy, etc. As a control parameter increases, the number of chaotic phases also increases. Therefore, intermittency presents a continuous route from regular behavior to chaotic motion. In this paper, a review of different types of intermittency is carried out. In addition, the description of two recent formulations to evaluate the reinjection processes is developed. The new theoretical formulations have allowed us to explain several tests previously called pathological. The theoretical background also includes the noise effects in the reinjection mechanism. Full article

The volatility and intermittency of wind energy result in highly unpredictable wind power output, which poses challenges to the stability of the intact power system when integrating large-scale wind power. The accuracy of wind power prediction is critical for maximizing the utilization of wind energy, improving the quality of power supply, and maintaining the stable operation of the power grid. To address this challenge, this paper proposes a novel hybrid forecasting model, referred to as Hybrid WT–PSO–NARMAX, which combines wavelet transform, randomness operator-based particle swarm optimization (ROPSO), and non-linear autoregressive moving average with external inputs (NARMAX). The model is specifically designed for power generation forecasting in wind energy systems, and it incorporates the interactions between the wind system’s supervisory control and data acquisition’s (SCADA) actual power record and numerical weather prediction (NWP) meteorological data for one year. In the proposed model, wavelet transform is utilized to significantly improve the quality of the chaotic meteorological and SCADA data. The NARMAX techniques are used to map the non-linear relationship between the NWP meteorological variables and SCADA wind power. ROPSO is then employed to optimize the parameters of NARMAX to achieve higher forecasting accuracy. The performance of the proposed model is compared with other forecasting strategies, and it outperforms in terms of forecasting accuracy improvement. Additionally, the proposed Prediction Error-Based Power Forecasting (PEBF) approach is introduced, which retrains the model to update the results whenever the difference between forecasted and actual wind powers exceeds a certain limit. The efficiency of the developed scheme is evaluated through a real case study involving a 180 MW grid-connected wind energy system located in Shenyang, China. The proposed model’s forecasting accuracy is evaluated using various assessment metrics, including mean absolute error (MAE) and root mean square error (RMSE), with the average values of MAE and RMSE being 0.27% and 0.30%, respectively. The simulation and numerical results demonstrated that the proposed model accurately predicts wind output power. Full article

The theory of critical curves determines the main characteristics of a discrete dynamical system in two dimensions. One important property that has garnered recent attention is the problem of chaos synchronization, along with the location of its chaotic attractors, basin boundaries, and bifurcation mechanisms. Varying the parameters of the maps reveals the instrumental role that these curves play, where the bifurcation leads to complex topological structures of the basins occurs by contact with the basin boundaries, resulting in the appearance or disappearance of some components of the basin. This study focuses on the properties of a discrete dynamical system consisting of two symmetrically coupled non-invertible maps, specifically those with an invariant one-dimensional submanifold (or one-dimensional maps). These maps exhibit a complex structure of basins with the coexistence of symmetric chaotic attractors, riddled basins, blow-out, on-off intermittency, and, most significantly, the appearance of chaotic synchronization with a correlation between all the characteristics. The numerical method of critical curves can be used to demonstrate a wide range of dynamic scenarios and explain the bifurcations that lead to their occurrence. These curves play a crucial role in a system of two symmetrically coupled maps, and their significance will be discussed. Full article

In this paper, based on a three-dimensional Bao system, a memristor-based hyper-chaotic Bao-like system is successfully constructed, and a simulated equivalent circuit is designed, which is used to verify the chaotic behaviors of the system. Meanwhile, a control method called periodically intermittent control with variable control width is proposed. The control width sequence in the proposed method is not only variable, but also monotonically decreasing, and the method can effectively stabilize most existing nonlinear systems. Moreover, the memristor-based hyper-chaotic Bao-like system is controlled by combining the proposed method with the Lyapunov stability principle. Finally, we should that the proposed method can effectively control and stabilize not only the proposed hyper-chaotic system, but also the Chua’s oscillator. Full article

The geographically spatial and controlled distribution of fossil fuel resources, catastrophic global warming, and depletion of fossil fuel resources have forced us to integrate zero- or low-emissions energy resources, such as wind and solar, in the generation mix. These renewable energy resources are unexhausted, available around the globe, and free of cost. The advancement in wind and solar technologies has caused an appreciable decrease in installed the and global levelized costs of electricity via these sources. Therefore, the penetration of renewable energy resources in the generation mix can provide a promising solution to the above-mentioned problems. The aim of simultaneously reducing fuel consumption in terms of “Fuel Cost” and “Emission” in thermal power plants is called a combined economic emission dispatch problem. It is a combinatorial and multi-objective optimization problem. The solution of this problem is to allocate the load demand and losses on the committed units in such way that the overall costs of the generation and emission of thermal units are reduced, while the legal bounds (constraints) are met. It is a highly non-linear and complex optimization problem. The valve-point loading effect makes this problem non-convex. The addition of renewable energy resources (RERs) adds more complexities to this problem because they are intermittent. In this work, chaotic salp swarm algorithms (CISSA) are used to solve the combined economic emission dispatch problem. Chaos is used as an alternative to randomization for the tuning of the control variable to improve the trait of obtaining global extrema. Different test cases having different combinations of thermal, solar, and wind units are solved using the proposed algorithm. The results show the superiority of this study in comparison to the existent research results in terms of the cost of generation and emissions. Full article

We study dynamics of a unidirectional ring of three Rulkov neurons coupled by chemical synapses. We consider both deterministic and stochastic models. In the deterministic case, the neural dynamics transforms from a stable equilibrium into complex oscillatory regimes (periodic or chaotic) when the coupling strength is increased. The coexistence of complete synchronization, phase synchronization, and partial synchronization is observed. In the partial synchronization state either two neurons are synchronized and the third is in antiphase, or more complex combinations of synchronous and asynchronous interaction occur. In the stochastic model, we observe noise-induced destruction of complete synchronization leading to multistate intermittency between synchronous and asynchronous modes. We show that even small noise can transform the system from the regime of regular complete synchronization into the regime of asynchronous chaotic oscillations. Full article

Wind energy forecasting is a critical aspect for wind energy producers, given that the chaotic nature and the intermittence of meteorological wind cause difficulties for both the integration and the commercialization of wind-produced electricity. For most European producers, the quality of the forecast also affects their financial outcomes since it is necessary to include the impact of imbalance penalties due to the regularization in balancing markets. To help wind farm owners in the elaboration of offers for electricity markets, the EOLO predictor model can be used. This tool combines different sources of data, such as meteorological forecasts, electric market information, and historic production of the wind farm, to generate an estimation of the energy to be produced, which maximizes its financial performance by minimizing the imbalance penalties. This research study aimed to evaluate the performance of the EOLO predictor model when it is applied to the different Spanish electricity markets, focusing on the statistical analysis of its results. Results show how the wind energy forecast generated by EOLO anticipates real electricity generation with high accuracy and stability, providing a reduced forecast error when it is used to participate in successive sessions of the Spanish electricity market. The obtained error, in terms of RMAE, ranges from 8%, when it is applied to the Day-ahead market, to 6%, when it is applied to the last intraday market. In financial terms, the prediction achieves a financial performance near 99% once imbalance penalties have been discounted. Full article