The use of wind power is rapidly increasing as an important part of power systems, but because of the intermittent and random nature of wind speed, system operators and researchers urgently need to find more reliable methods to forecast wind speed. T...
Nonlinear systems of equations arise across engineering, physics, and biological modeling; however, classical Newton-type methods may fail when the initial approximation lies outside the convergence region of the NJN local solver. This work proposes...
Hybrid power technology for ships is an effective way to promote the green and low-carbon development of the maritime industry. The development of pattern recognition technology provides new research ideas for the rational allocation and utilization...
This work investigates the macroscopic behavior of skeletal muscles from a system-theoretic perspective. Based on data available in the literature, we propose an initial evaluation model for isometric force generation, i.e., force produced at a const...
In order to deal with control constraints and the performance optimization requirements in aircraft engines, a new nonlinear model predictive control method based on an elastic BP neural network with a hybrid grey wolf optimizer is proposed in this p...
Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel h...
Reliable digital twins of complex nonlinear systems require not only high predictive accuracy but also physical consistency, robustness under degraded operating conditions, and explicit uncertainty handling. Purely data-driven models often suffer fro...
Parallel electric–hydraulic hybrid (PEHH) powertrains offer benefits of lower energy consumption and increased battery lifetime compared to pure electric ones. These merits can be extended with advanced control methods that optimally deploy on-...
Accurate wind speed forecasting is a significant factor in grid load management and system operation. The aim of this study is to propose a framework for more precise short-term wind speed forecasting based on empirical mode decomposition (EMD) and h...
This study presents a multi-objective predictive energy management system (EMS) for optimizing hybrid renewable energy systems (HRES) in autonomous marine vessels. The objective is to minimize fuel consumption and emissions while maximizing renewable...
In the search for causes and cures of cancer diseases, many mathematical models developed have resulted in systems of nonlinear stiff ordinary differential equations. With these models, many numerical estimates of biological knowledge of the paramete...
This paper proposes a novel hybrid data-driven modeling method for missiles. Based on actual flight test data, the missile hybrid model is established by combining neural networks and the mechanism modeling method, considering the uncertainties and n...
Short vehicle speed prediction is important in predictive energy management strategies, and the accuracy of the prediction is beneficial for energy-saving performance. However, the nonlinear feature of the speed series hinders the improvement of pred...
A conditional Gaussian framework for understanding and predicting complex multiscale nonlinear stochastic systems is developed. Despite the conditional Gaussianity, such systems are nevertheless highly nonlinear and are able to capture the non-Gaussi...
The complex features of carbon price, such as volatility and nonlinearity, pose a serious challenge to accurately predict it. To this end, this paper proposes a novel forecasting framework for carbon emission trading price based on nonlinear integrat...
Foreign exchange rate forecasting is difficult, as its time series have complex nonlinear and nonstationary dynamics. To this end, the paper proposes a hybrid forecasting framework based on kernel regression decomposition that combines nonparametric...
Hydraulic oscillator tools (HOTs) are effective solutions for mitigating excessive drag encountered during sliding drilling in horizontal wells. However, their field performance remains unpredictable due to theoretical limitations in modeling nonline...
This study investigates physics-informed and data-driven hybrid modeling strategies for an automotive-grade electrohydraulic (EH) semi-active damper system. Although deep sequence learning architectures such as Long Short-Term Memory (LSTM) networks...
Accelerometers built on micro-electromechanical systems (MEMS) play a critical role in structural monitoring and machinery diagnostics; however, their accuracy suffers from intrinsic nonlinearities—dead zones, hysteresis, saturation, and colore...
Most control strategies for magnetic bearings are typically formulated upon the linearized suspension force model, and the nonlinear characteristics are neglected or regarded as the disturbance and variation in parameters of the control system. The c...
Modeling and forecasting nonstationary and nonlinear economic time series remain fundamentally challenging due to structural breaks, volatility clustering, and noise contamination that distort the intrinsic stochastic structure. To address these limi...
Most European Union governments and numerous railway operators have announced plans to replace most of their diesel units by 2030–2040. However, a significant portion of the rail network remains non-electrified. In some cases, the proposed solu...
Target location is the basic application of a multistatic sonar system. Determining the position/velocity vector of a target from the related sonar observations is a nonlinear estimation problem. The presence of possible sensor position uncertainties...
To obtain high-quality Pareto optimal solutions and to enhance the searchability of the biogeography-based optimization (BBO) algorithm, we present an improved BBO algorithm based on hybrid migration and a dual-mode mutation strategy (HDBBO). We firs...
Path-dependent dynamic systems are often analysed through separate metric, stochastic, econometric or nonlinear methods, while their integration within a unified architecture remains limited. This study develops and validates a Hybrid Dynamic Modelli...
This study focuses on quantifying wind–temperature load combination coefficients for long-span hybrid cable-stayed suspension bridges (HCSSBs) to overcome limitations of traditional methods in ignoring load correlation and geometry nonlinearity...
This paper develops a wave-to-wire model of a vibro-impact wave energy converter array for stand-alone offshore applications. Nonlinear model predictive control is proposed for maximising the wave power capture of the array, and implemented by AC/DC...
The paper presents simulation and experimental verification of the hybrid magnetic bearing (HMB) performance characteristics. It has been demonstrated that the additional errors from the eddy current sensors have a significant impact on the control s...
This study designs a two-stage algorithm to address the bid generation problem of carriers when adding new vehicle routes in the presence of the existing vehicle routes to provide transportation service. To obtain the best auction combination and bid...
Existing truck–drone collaborative routing models predominantly assume fixed flight speeds, overlooking the non-linear coupling among speed, payload, and energy consumption, which limits urban delivery efficiency. To bridge this gap, this paper...
Seedling growth is an essential stage for the future growth of trees, and Paulownia is treated as a rapidly growing hardwood tree species with wide industrial application. There is a knowledge gap in the modeling and analysis of the seedling growth p...
Liquid-lubricated spherical spiral-groove hydrodynamic bearings (SSGB) sustain composite loads under high-speed conditions. This study establishes a numerical framework for SSGB lubrication and proposes a physics-guided hybrid surrogate model to acce...
The ultraviolet water disinfection process has numerous advantages in comparison with other water treatments. This study deals with this process’s optimal control and presents all its stages, from problem statement to analysis of results. We consider...
A dynamically operated sorption-enhanced water–gas shift reactor is modelled to leverage its performance by means of model-based process design. This reactor shall provide CO2-free synthesis gas for e-fuel production from pure CO. The nonlinear model...
In this article, we study the vibration performance of multiscale hybrid nanocomposite (MHC) annular plates (MHCAP) resting on Winkler–Pasternak substrates exposed to nonlinear temperature gradients. The matrix material is reinforced with carbo...
Sustained uphill motion of two-wheeled self-balancing wheelchairs is challenging. Slope-induced gravity changes the equilibrium condition, driving-torque demand, and velocity response. This paper proposes a smoothly weighted hybrid control method. Th...
Reservoir management, flood control, and operational planning are the benefits of dam inflow forecasting. Decomposition algorithms can decompose complex inflow data into intrinsic components and reduce noise and fluctuations, while quantum machine le...
Electric vehicles (EVs) are widely applied in logistics companies’ urban logistics distribution, as fuel prices increase and environmental awareness grows. This study introduces an EV charging station (CS) location-routing problem with time win...
A new methodology, called HY4RES models, includes hybrid energy solutions (HESs) based on the availability of renewable sources, for 24 h of water allocation, using WaterGEMS 10.0 and PVGIS 5.2 as auxiliary calculations. The optimization design was a...
Hybrid-excited permanent-magnet-assisted synchronous reluctance machines require coupled control of stator and field currents. This study compares three established model-following current-control placements, m, p, and mp, using a common nonlinear pl...
Reliable electric load forecasting is an important engineering problem for power-system planning, grid stability, and mission-critical energy management. This paper presents a symmetry-aware simulation and modeling framework for medium-range electric...
The global transportation sector is rapidly shifting towards electrification, aiming to create more sustainable environments. As a result, there is a significant focus on optimizing performance and increasing the lifespan of batteries in electric veh...
In nonlinear Model Predictive Control (MPC) algorithms, the number of cost-function evaluations and the resulting calculation time depend on the initial solution to the nonlinear optimisation task. Since calculations must be performed fast on-line, t...
The staged fracturing horizontal well has proven to be an attractive alternative for improving the development effect of a low permeability waterflood reservoir. Due to the coexistence of matrix, fracture, and horizontal wellbore, it remains a great...
Soil moisture time series data are usually nonlinear in nature and are influenced by multiple environmental factors. The traditional autoregressive integrated moving average (ARIMA) method has high prediction accuracy but is only suitable for linear...
In this paper, we study the outage and throughput performance for the simultaneous wireless information and power transfer (SWIPT) cooperative decode-and-forward (DF) communication systems. The hybrid receiver that uses both time switching (TS) and p...
Reducing energy consumption and carbon emissions from ships is a major concern. The development of hybrid technologies offers a new direction for the rational distribution of energy. Therefore, this paper establishes a torque model for internal combu...
To address challenges in stock price prediction including data nonlinearity and anomalies, we propose a hybrid CNN-BiGRU-AM framework integrated with deep learning-based anomaly detection. First, an anomaly detection module identifies irregularities...
In the present paper, a novel approach for image denoising based on the numerical solution to the nonlinear diffusion equation is proposed. The Perona–Malik-type equation is solved by employing a hybrid lattice Boltzmann model with five discret...
This paper re-analyzes the nonlinear Susceptible–Infected–Recovered (SIR) model using a hybrid approach based on the Laplace–Padé technique. The proposed approach is successfully applied to extract several analytic approximat...
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