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The feed axis system of computer numerical control (CNC) machine tool is affected by temperature changes and axial loads during the machining process, which reduces the position accuracy of CNC machine tools. Due to the complexity of processing conditions and the difficulty in error detection, the formation mechanism of position error in actual working conditions is still vague. The purpose of this paper is to investigate the evolution of position error under thermal–mechanical coupling loads and identify, evaluate, and predict the position error. First, the formation mechanism and influencing factors of position error are clarified through theoretical analysis. Secondly, based on cluster analysis, the distribution of temperature measurement points is optimized to select the thermal key points which best reflect the impact between temperatures and errors. Finally, experimental data are used to decompose and evaluate the evolution process of the position error curve and the motion state of the feed axis, radial basis function neural network (RBFNN) is employed to model and predict the position error under thermal–mechanical coupling loads. The findings of this paper can help trace the source of position error and accurately assess the operating status of the machine tool. [ABSTRACT FROM AUTHOR]

The handheld diagnosis and analysis are highly dependent on the physiological data in the clinical sector. Detection of the defect in the neuronal-assisted activity raises the challenge to the prevailing treatment that benefits from machine learning approaches. The congregated EEG data is then utilized in design of learning applications to develop a model that classifies intricate EEG patterns into active and inactive segments. During arithmetic problem-solving EEG signal acquired from frontal lobe contributes for intelligence detection. The low intricate statistical parameters help in understanding the objective. The mean of the segmented samples and standard deviation are the features extracted for model building. The feature selection is handled using correlation and Fisher score between {Fp1 and F8} and priority ranking of the regions with enhanced activity are selected for the classifier models to the training net. The R-studio platform is used to classify the data based on active and inactive liability. The radial basis function kernel for support vector machine (SVM) is deployed to substantiate the proposed methodology. The vulnerable regions F1 and F8 for arithmetic activity can be visualized from the correlation fit performed between regions. Using SVM classifier sensitivity of 92.5% is obtained for the selected features. A wide range of clinical problems can be diagnosed using this model and used for brain-computer interface. [ABSTRACT FROM AUTHOR]

Roundabouts capacity is a critical aspect when assessing the feasibility of constructing them. This research examines 50 entry lanes of 15 roundabouts in Greece, both single-lane and multilane. Traffic flows, geometric parameters, and gap acceptance parameters were measured and calculated based on field observations. A quadcopter unmanned aerial vehicle (UAV), RTK GNSS receiver, and video camera attached to a tripod were used to perform the field surveys. Photogrammetry techniques were used to extract the data required for the analysis. The development and evaluation of capacity prediction models involve the implementation of both multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Based on the findings, the current models of Greek and international standards overestimate roundabout capacity. The developed MLP model predicts the existing entry capacity more accurately compared to the RBF model. The developed model can be generalized and the evaluation metrics (R2=0.87 and RMSE=138) indicate that its predictive ability is quite high. [ABSTRACT FROM AUTHOR]

Radial basis function finite difference (RBF-FD) discretization has recently emerged as an alternative to classical finite difference or finite element discretization of (systems) of partial differential equations. In this paper, we focus on the construction of preconditioners for the iterative solution of the resulting linear systems of equations. In RBF-FD, a higher discretization accuracy may be obtained by increasing the stencil size. This, however, leads to a less sparse and often also worse conditioned stiffness matrix which are both challenges for subsequent iterative solvers. We propose to construct preconditioners based on stiffness matrices resulting from RBF-FD discretization with smaller stencil sizes compared to the one for the actual system to be solved. In our numerical results, we focus on RBF-FD discretizations based on polyharmonic splines (PHS) with polynomial augmentation. We illustrate the performance of smaller stencil preconditioners in the solution of the three-dimensional convection-diffusion equation. [ABSTRACT FROM AUTHOR]

We introduce a novel numerical method via a class of radial basis function-produced finite difference solvers, applicable to both interpolation and partial differential equation (PDE) problems. The method leverages integrals of the Gaussian kernel, introducing new weights for problem-solving. Analytical solutions to approximate the derivatives of a function are derived and computed on a stencil with both non-uniform and uniform distances. Our observations indicate that the analytical weights exhibit greater stability compared to the numerical weights when addressing problems. In the final step, we use the derived formulations to solve a multi-dimensional option pricing problem in finance. The results demonstrate that our proposed numerical method outperforms in terms of numerical accuracy across grids of different sizes. Given the multi-dimensional nature of the dealing model, which involves handling a basket of assets, our approach becomes particularly relevant for assessing and managing financial risks. [ABSTRACT FROM AUTHOR]

In this article, we introduce a numerical method that combines local radial basis functions partition of unity with backward differentiation formula to efficiently solve linear and nonlinear fractional integro-differential equations on two-dimensional regular and irregular domains. We derive the time-discretized formulation using the backward difference formula. The meshless radial basis function method, particularly the radial basis function partition of unity method, offers advantages such as flexibility, accuracy, ease of implementation, adaptive refinement, and efficient parallelization. We apply the radial basis function partition of unity method to spatially discretize the problem using the scaled Lagrangian form of polyharmonic splines as approximation bases. Numerical simulations demonstrate the efficacy of our method in solving linear and nonlinear fractional integro-differential equations with complex domains and smooth and nonsmooth initial conditions. Comparative analysis confirms the superior performance of our proposed method. [ABSTRACT FROM AUTHOR]

The efficiency of communication processing and control depends heavily on the recognition of network signals, however irregularities and mistakes frequently arise during the application process. In this work, we leverage machine learning models to automatically identify computer network communication signals, leveraging recent advancements in artificial intelligence technology. In the simulation, we employed a support vector machine (SVM) model, and we utilized parameter optimization to address the overlearning issue. The process of classifying modulation signals involves the extraction of feature parameters through the application of support vector machine and radial basis function neural network (RBFNN) models, respectively. Real-world network communication involves the observation and collection of signals from various viewpoints or feature spaces. These views provide a variety of detailed insights into the signal, and feature extraction is carried out for each view to produce the associated feature vectors. An extensive description of the signal can be generated by extracting the features from several viewpoints. Various viewpoints' feature vectors are combined and synthesized. The robustness of signal recognition can be increased and the bias and inaccuracy that could be generated by a single view can be minimized by combining the data from several perspectives. The support vector machine performs better than the radial basis function neural network, according to experimental findings. When the signal-to-noise ratio (SNR) is high, network communication signals function effectively. However, the latter (RBFNN) performs significantly worse in low SNR settings whilst the former (SVM) retains good accuracy. Therefore, when it comes to computer network communication signals, the support vector machine model is thought to be more reliable. [ABSTRACT FROM AUTHOR]

This paper introduces a novel control strategy for managing the uncertainties in flexible joint manipulators, incorporating a Radial Basis Function Neural Network (RBFNN) with Adaptive Dynamic Surface Control (ADSC). This strategy innovatively utilizes RBFNN to precisely approximate uncertain system dynamics and integrates a nonlinear damping term to effectively counteract external disturbances, enhancing the overall control accuracy. We have also developed an adaptive law that updates neural network weights and system parameters in real-time, ensuring the system's adaptability to dynamic changes. The application of the Lyapunov method ensures that all signals within the closed-loop system remain semi-globally uniformly bounded, significantly reducing tracking errors. Moreover, we introduce the use of Long Short-Term Memory (LSTM) networks for predictive analysis of state data, which further confirms the robustness and effectiveness of our control method through extensive simulations. The distinctive integration of these technologies and their practical validation through comparative simulations underscore the innovative aspects of our approach in addressing real-world challenges in flexible manipulators. [ABSTRACT FROM AUTHOR]

In the Pu'er tea market, the ubiquity of blending different varieties and the fraudulent representation of vintage years present a persistent challenge. Traditional sensory evaluation and experience are often inadequate for discerning the true variety and vintage of tea, highlighting the need for more sophisticated analytical methods to ensure authenticity and quality. Fourier transform near infrared diffuse reflectance spectroscopy combined with radial basis function neural network (RBFNN) was applied for determination of the varieties and vintages of Pu'er tea. For vintage identification, the accuracy, precision, recall, and F1-score of the RBFNN model for the prediction set were 99.2%, 98.2%, 98.0%, and 98.0%, respectively. For identification of varieties adulteration, the corresponding parameters were 98.9%, 97.2%, 96.7%, and 96.6%, respectively. These results illustrated the feasibility to identify the adulteration of varieties and misrepresentation of vintages of Pu'er tea with near infrared spectra and RBFNN model, proving an efficient alternative for Pu'er tea quality inspection, and offering a robust method for combating the pervasive issues within the market. [ABSTRACT FROM AUTHOR]

Switched reluctance motor drives are becoming attractive for electric vehicle propulsion systems due to their simple and cheap construction. However, their operation is degraded by torque ripples due to the salient nature of the stator and rotor poles. There are several methods of mitigating torque ripples in switched reluctance motors (SRMs). Apart from changing the geometrical design of the motor, the less costly technique involves the development of an adaptive switching strategy. By selecting suitable turn-on and turn-off angles, torque ripples in SRMs can be significantly reduced. This work combines the benefits of Harris Hawks Optimization (HHO) and Radial Basis Functions (RBFs) to search and estimate optimal switching angles. An objective function is developed under constraints and the HHO is utilized to perform search stages for optimal switching angles that guarantee minimal torque ripples at every speed and current operating point. In this work, instead of storing the θ o n ,   θ o f f   values in a look-up table, the values are passed on to an RBF model to learn the nonlinear relationship between the columns of data from the HHO and hence transform them into high-dimensional outputs. The values are used to train an enhanced neural network (NN) in an adaptive switching strategy to address the nonlinear magnetic characteristics of the SRM. The proposed method is implemented on a current chopping control-based SRM 8/6, 600 V model. Percentage torque ripples are used as the key performance index of the proposed method. A fuzzy logic switching angle compensation strategy is implemented in numerical simulations to validate the performance of the HHO-RBF method. [ABSTRACT FROM AUTHOR]

AbstractPrefrontal Cortex-Directional Rhythms (PFC-DR) classification plays a significant role in Brain-Computer Interface (BCI) research since it is crucial for the effective rehabilitation of injured voluntary movements. The primary aims of this study are to conduct a thorough examination of traditional classification techniques, while emphasizing the significance of radial basis functions within support vector machine (RBF-SVM) based approaches in the context of BCI systems. Consequently, in contrast to existing machine learning-based approaches, this generalized RBF-SVM classifier effectively identified observed data with an overall 96.91% accuracy validated with a 10-fold repeated random train test split cross validation technique using confusion matrix analysis. [ABSTRACT FROM AUTHOR]

Melt viscosity is regarded as a key quality indicator of the polymer melt in polymer extrusion processes. However, limitations such as disturbances to the melt flow and measurement delays of the existing in-line and side-stream rheometers prevent the monitoring and controlling of this key parameter in real time. Soft sensors can be employed to monitor physical parameters that are difficult to measure using hardware sensing instruments. This study presents a grey-box soft sensing solution to predict the melt viscosity in real time, which combines physics-based knowledge with machine learning. A fine-tuned physics-based mathematical model is used to make melt viscosity predictions, and a deep neural network is employed to compensate for its prediction errors. The proposed soft sensor model reported a normalised root mean square error of 2.2 10−3 (0.22%), outperforming fully data-driven soft sensor models based on multilayer perceptron and long short-term memory neural networks. Furthermore, it exhibited an improvement of approximately 95% in terms of predictive performance, compared to a soft sensor based on a radial basis function neural network reported in a previous study. The proposed soft sensor can monitor viscosity changes caused by changes in operating conditions but not suitable for detecting viscosity changes due to changes in material properties. The findings of this study can aid in enhancing process monitoring and control in polymer extrusion processes. [ABSTRACT FROM AUTHOR]

Modeling unsaturated flow through soils with water uptake by plant root has many applications in agriculture and water resources management. In this study, our aim is to develop efficient numerical techniques for solving the Richards equation with a sink term due to plant root water uptake. The Feddes model is used for water absorption by plant roots, and the van-Genuchten model is employed for capillary pressure. We introduce a numerical approach that combines the localized exponential radial basis function (EXP-RBF) method for space and the second-order backward differentiation formula (BDF2) for temporal discretization. The localized RBF methods eliminate the need for mesh generation and avoid ill-conditioning problems. This approach yields a sparse matrix for the global system, optimizing memory usage and computational time. The proposed implicit EXP-RBF techniques have advantages in terms of accuracy and computational efficiency thanks to the use of BDF2 and the localized RBF method. Modified Picards iteration method for the mixed form of the Richards equation is employed to linearize the system. Various numerical experiments are conducted to validate the proposed numerical model of infiltration with plant root water absorption. The obtained results conclusively demonstrate the effectiveness of the proposed numerical model in accurately predicting soil moisture dynamics under water uptake by plant roots. The proposed numerical techniques can be incorporated in the numerical models where unsaturated flows and water uptake by plant roots are involved such as in hydrology, agriculture, and water management. • We propose implicit EXP-RBF techniques for infiltration in soils with water uptake by plant roots. • Localized exponential radial basis function method is used in space. • BDF2 is used for temporal discretization. • The proposed techniques have advantages in terms of accuracy and computational efficiency. • The numerical model is accurate in predicting soil moisture under water uptake by plant roots. [ABSTRACT FROM AUTHOR]

• Construct a new MQ quasi-interpolant. • Prove the good properties of the MQ quasi-interpolant. • Prove the error estimation of the new method. • Compare the quasi-interpolant with the classical ones. • Apply the new method to numerical solution of Burgers' equation The Multi-Quadrics (MQ) radial basis function (RBF) quasi-interpolant has received widespread attention due to its simplicity and convenience, avoiding the possible ill-conditioning problem that may occur if there are a lot of interpolation points, and being able to directly provide numerical approximation results. We present a new quasi-interpolant L N for scattered data and prove that it has the property of linear reproducing and high computational accuracy, and does not require the first derivative values at the two endpoints, making it easier to use. Finally, the numerical scheme for solving Burgers' equation is presented, and numerical experiments are carried out and compared with other methods. The numerical results verify the effectiveness and accuracy of the new quasi-interpolant L N. [ABSTRACT FROM AUTHOR]

Hydrogen's role as a clean energy source necessitates accurate modeling of its combustion properties, particularly flame length, to ensure safety and efficiency in industrial applications. Traditional methods that rely solely on Froude number correlations often fail to capture the complexities of hydrogen jet flames, especially under-expanded conditions. This study overcomes these limitations by employing artificial neural networks (ANNs) with novel input parameters to predict hydrogen flame lengths. Two ANN models were developed: the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF) network. The MLP model achieved a regression coefficient (R2) of 0.990, while the RBF model demonstrated superior performance with an R2 of 0.996. Sensitivity analysis identified the Froude number as the most critical parameter influencing flame length. Experimental data from various nozzle diameters and flow rates were used for model training and validation. The application of Response Surface Methodology (RSM) further enhanced the model's predictive accuracy and addressed the limitations of single-parameter approaches. [Display omitted] • ANN models predict hydrogen flame length using dimensionless parameters. • RBF networks outperform MLP in accuracy and generalizability. • Sensitivity analysis highlights the Froude number's critical influence. • RSM improves model generalizability for hydrogen flame predictions. [ABSTRACT FROM AUTHOR]

Addressing the spatial variability, temporal dynamics, and non-linearity characteristics of port water levels, a hybrid prediction scheme was proposed, which integrates empirical mode decomposition (EMD) with a radial basis function neural network (RBFNN), optimized using the particle swarm optimization (PSO) algorithm. First, through the application of EMD, the port water level time series was decomposed into sub-series characterized by lower non-linearity. Subsequently, PSO was applied to fine-tune the center and spread parameters of the RBFNN, thereby enhancing the model's predictive performance. The optimized PSO-RBFNN model was employed to make predictions on the decomposed sub-series. Finally, reconstruction of the predicted sub-series yielded the final water level predictions. The feasibility and effectiveness of the proposed model were validated using measured port water level data. Results from simulations highlighted the model's ability to deliver accurate predictions across various lead times. Furthermore, comparative analysis revealed that the proposed model outperforms alternative methods in port water level prediction. Therefore, the proposed model serves as a reliable, efficient, and real-time prediction tool, providing robust support for port operational safety. [ABSTRACT FROM AUTHOR]

This research investigates the adsorption desulfurization of liquid fuels using artificial neural networks (ANN) and response surface methodology (RSM) approaches. The effectiveness of sulfur removal was predicted by analyzing five important factors: temperature, concentration, surface area, fuel/adsorbent, and time. We employed radial basis function (RBF) and multilayer perceptron (MLP) algorithms for ANN modeling. The optimal MLP configuration, utilizing the Levenberg–Marquardt (Trainlm) algorithm, consisted of three hidden layers with 20, 17, and 9 neurons, respectively, while the optimal RBF network contained 43 neurons. The MLP network's determination coefficient (R2) was 0.98 over 30 epochs, and its mean squared error (MSE) was 0.0028. The RBF network also obtained an R2 of 0.98 and an MSE of 0.0026 over 40 epochs. A two-factor interaction design served as the basis for the RSM model, which produced an R2 of 0.91. A comparison of the RSM, MLP, and RBF models, using the average absolute relative deviation, indicated that the ANN models, particularly the RBF model, produced more accurate predictions than the RSM model. The findings show that temperature and concentration were the two most significant factors influencing sulfur removal efficiency. Overall, artificial neural networks outperformed the RSM approach in predicting desulfurization performance, providing a more reliable modeling tool for optimizing the sulfur removal process. [ABSTRACT FROM AUTHOR]

This study introduces an innovative multi-objective optimization method based on sequential approximation optimization (SAO) and radial basis function (RBF) networks to enhance the injection molding process for liquid silicone optical lenses. The method successfully minimizes residual stress and volume shrinkage, thereby improving product quality and manufacturing efficiency. By replacing finite element reanalysis with the RBF network, it constructs an approximate functional relationship between process conditions and quality. The novelty lies in simplifying multi-objective optimization into a single-objective problem and utilizing Pareto boundary analysis for precise parameter tuning. This approach not only reduces trial-and-error costs and material waste but also significantly decreases carbon emissions, showcasing extensive potential for application in various manufacturing processes. Simulations varying key parameters—filling time, melt temperature, mold temperature, curing pressure, and curing time—revealed optimal conditions: filling time of 1.57s, melt temperature of 27.18 °C, mold temperature of 150 °C, curing time of 20.02s, and curing pressure of 28.79 MPa. Experiments were conducted to validate the numerical results, employing nondestructive testing methods to assess residual stress and volume shrinkage. The results demonstrated significant reductions in these values, affirming the method's reliability and practicality. This innovative and efficient optimization approach provides a robust solution for enhancing injection molding processes while contributing to sustainability and cost efficiency. [ABSTRACT FROM AUTHOR]

Background: Particulate matter less than 2.5 microns in aerodynamic diameter (PM2.5) is a significant air pollutant known to adversely affect respiratory health and increase the incidence of chronic obstructive pulmonary disease (COPD). Furthermore, climate change exacerbates these impacts, as extreme temperatures and relative humidity (RH) levels can intensify the effects of PM2.5. This study aims to examine the joint effects of PM2.5, temperature, and RH on the risk of COPD. Methods: A case–control study was conducted among 1,828 participants from 2017 to 2022 (995 COPD patients and 833 controls). The radial basis function interpolation was utilized to estimate participants' individual mean and differences in PM2.5, temperature, and RH in 1-day, 7-day, and 1-month periods. Logistic regression models examined the associations of environmental exposures with the risk of COPD adjusting for confounders. Joint effects of PM2.5 by quartiles of temperature and RH were also examined. Results: We observed that a 1 µg/m3 increase in PM2.5 7-day and 1-month mean was associated with a 1.05-fold and 1.06-fold increase in OR of COPD (p < 0.05). For temperature and RH, we observed U-shaped effects on OR for COPD with optimal temperatures identified as 21.2 °C, 23.8 °C, and 23.8 °C for 1-day, 7-day, and 1-month mean temperature, respectively, and optimal RH levels identified as 73.8%, 76.7%, and 75.4% for 1-day, 7-day, and 1-month mean RH, respectively (p < 0.05). The joint effect models show that high temperatures (> 23.5 °C) and both extremely low (69.3%) and high (80.9%) RH levels generally exacerbate the effects of PM2.5 on OR for COPD, especially over longer exposure durations. Conclusion: The joint effects of PM2.5, temperature, and RH on the risk of COPD underscore the importance of air pollution control and comprehensive research to mitigate COPD risk in the context of climate change. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach for addressing the Poisson equation in simply and doubly connected 3D domains with irregular surfaces, which has significant implications in various scientific and engineering fields, such as irregular cross-section optical waveguides and electromagnetic wave propagation. The Poisson equation is extensively utilized across disciplines like physics, engineering, and mathematics, and its solution offers insight into diverse physical phenomena. The solution to the Poisson equation is helpful in constructing potentials crucial for the comprehension and design of optical and electromagnetic systems. The application of Radial Basis Functions (RBFs) collocation method with changeable form parameters presents novel opportunities for precise and efficient resolutions of this significant equation. Our methodology is relevant to both simply and doubly connected three-dimensional domains with irregular surfaces, frequently seen in various practical applications, such as complex waveguide geometries. Seven instances are presented for various complex simply and doubly connected 3D domains, illustrating the efficacy of the suggested Poisson solver in generating potentials to improve the precision and efficiency of the method. The proposed method can be considered as a benchmark solver for such type of problems appearing in optics and electromagnetic wave engineering. keyword: Radial Basis Functions, Simply Connected Domains, Double Connected Domains, Variable shape parameter, Three dimensional Laplace equation, Three dimensional Poisson Equation. [ABSTRACT FROM AUTHOR]

The deployment of grid connected photovoltaic (PV) systems has become increasingly vital in the pursuit of sustainable and renewable energy sources. As the global demand for electricity rises, the efficient and reliable incorporation of PV power into electrical grid is of paramount importance. An elementary Luo converter is employed here to enhance the resultant voltage of PV array. To further improve the system's performance, a Grey Wolf optimized radial basis function neural network (GWO-RBFNN) is employed for maximum power point tracking (MPPT). The GWO algorithm is employed to fine-tune output of RBFNN, making it capable of adaptively extract maximum power. According to the obtained MPP, the input signals to the pulse width modulation generator is tuned using the proposed hybrid MPPT controller. These pulses regulates the operation of elementary Luo converter and guarantees maximum energy conversion efficiency. The converter's DC link voltage is subsequently subject into grid through a single-phase voltage source inverter which is synchronized with the grid. To facilitate seamless grid integration and synchronization, a conventional proportional integral (PI) controller is deployed. The simulation outputs attained using Matlab results in a robust and efficient system, capable of contributing reliable renewable energy to the grid. The tracking efficiency of the proposed hybrid MPPT controller reaches up to 98.1% and the THD value is reduced to 2.95% which indicates the power quality of the proposed system. [ABSTRACT FROM AUTHOR]

The compressibility factor indicates the deviation of the real natural gas from the ideal behavior. It is one of the most important parameters in the natural gas industry. In the present study, two different types of neural networks – multi-layer perceptron (MLP) and radial basis functions (RBF) – were used to predict the compressibility factor Z of natural gas. The pressure, temperature, and speed of sound (SoS) were chosen as input parameters for the artificial neural network (ANN) models. The training and testing of the MLP-ANN and RBF-ANN were carried out on the basis of 151 days of continuous measurements. Different variants of both types of neural networks were implemented and a comparative analysis of their modeling capabilities was performed. The models developed show a very high prediction accuracy, with the results obtained showing a certain advantage of the RBF-ANN. The comparative analysis was performed on the basis of standard performance indicators such as R2, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE). The present study shows an intelligent method implemented in two different variants to determine the compressibility factor of natural gas without the need to use the equation of state. [ABSTRACT FROM AUTHOR]

Sophisticated high-fidelity simulations can predict bone mass density (BMD) changes around a hip implant after implantation. However, these models currently have high computational demands, rendering them impractical for clinical settings. Model order reduction techniques offer a remedy by enabling fast evaluations. In this work, a non-intrusive reduced-order model, combining proper orthogonal decomposition with radial basis function interpolation (POD-RBF), is established to predict BMD distributions for varying implant positions. A parameterised finite element mesh is morphed using Laplace's equation, which eliminates tedious remeshing and projection of the BMD results on a common mesh in the offline stage. In the online stage, the surrogate model can predict BMD distributions for new implant positions and the results are visualised on the parameterised reference mesh. The computational time for evaluating the final BMD distribution around a new implant position is reduced from minutes to milliseconds by the surrogate model compared to the high-fidelity model. The snapshot data, the surrogate model parameters and the accuracy of the surrogate model are analysed. The presented non-intrusive surrogate model paves the way for on-the-fly evaluations in clinical practice, offering a promising tool for planning and monitoring of total hip replacements. [ABSTRACT FROM AUTHOR]

The motive of current work is to design a novel radial basis Bayesian regularization neural network (RB-BRNN) for solving the nonlinear fractional economic and environmental system (FEES). A radial basis activation function in the hidden layers is applied by taking 20 numbers of neurons. The mathematical FEES is presented in three classes, named as cost of control accomplishment, manufacturing elements competence and technical exclusion's diagnostics cost. A reference dataset is obtained using the Adams numerical results to reduce the mean square error (MSE) by taking the data for training 70%, while 15% is used for both testing and validation. The negligible absolute error values and comparison of the solutions develop the worth of computing RB-BRNN in order to solve the nonlinear dynamics of the FEES. Error diagrams, regression values, and the MSE performances are implemented to assess the precision of the designed solver. [ABSTRACT FROM AUTHOR]

The profile of Volatile Organic Compounds (VOCs) may help prioritise at-risk groups for early cancer detection. Urine sampling has been shown to provide good disease accuracy whilst being patient acceptable compared to faecal analysis. Thus, in this study, urine samples were examined using an electronic nose with metal oxide gas sensors and a solid-phase microextraction sampling system. A calibration dataset (derived from a previous study) with CRC-positive patients and healthy controls was used to train a radial basis function neural network. However, a blinded analysis failed to detect CRC accurately, necessitating an enhanced data-processing strategy. This new approach categorised samples by significant bowel diseases, including CRC and high-risk polyps. Retraining the neural network showed an area under the ROC curve of 0.88 for distinguishing CRC versus non-significant bowel disease (without CRC, polyps or inflammation). These findings suggest that, with appropriate training sets, urine VOC analysis could be a rapid, low-cost method for early detection of precancerous colorectal polyps and CRC. [ABSTRACT FROM AUTHOR]

This study investigates the synchronization and anti-synchronization of both identical and non-identical chaotic satellite systems. A fixed-time sliding mode control framework, based on a radial basis function (RBF) neural network, has been developed to synchronize the chaotic dynamics of master–slave satellite configurations. The proposed control scheme operates under the assumption that the dynamics of the satellites are not entirely known. The proposed control method guarantees that system errors will converge to negligible levels within a fixed time. Furthermore, the controller exhibits robustness in the presence of parametric uncertainties and external disturbances. The stability of the controlled systems is rigorously validated through Lyapunov analysis, and the controller's effectiveness is confirmed through extensive simulation studies. These simulations are conducted on both identical and non-identical satellite models, with performance comparisons made against recent findings reported in the literature. [ABSTRACT FROM AUTHOR]

This study introduces methods for generating fused precipitation data by applying radial basis function (RBF) interpolation, which integrates radar reflectivity-based data with ground-based precipitation gauge measurements. Rain gauges provide direct point rainfall measurements near the ground, while radars capture the spatial variability of precipitation. However, radar-based estimates, particularly for extreme rainfall events, often lack accuracy due to their indirect derivation from radar reflectivity. The study aims to produce high-resolution gridded ground precipitation data by merging radar rainfall estimates with the precise rain gauge measurements. Rain gauge data were sourced from automated synoptic observing systems (ASOSs) and automatic weather systems (AWSs), while radar data, based on hybrid surface rainfall (HSR) composites, were all provided by the Korea Meteorological Administration (KMA). Although RBF interpolation is a well-established technique, its application to the merging of radar and rain gauge data is unprecedented. To validate the accuracy of the proposed method, it was compared with traditional approaches, including the mean field bias (MFB) adjustment method, and kriging-based methods such as regression kriging (RK) and kriging with external drift (KED). Leave-one-out cross-validation (LOOCV) was performed to assess errors by analyzing overall error statistics, spatial errors, and errors in rainfall intensity data. The results showed that the RBF-based method outperformed the others in terms of accuracy. [ABSTRACT FROM AUTHOR]

In this article, a hierarchical distributed model‐free adaptive fault‐tolerant vehicular platooning control scheme for nonlinear vehicular platooning systems (VPSs) is studied. First of all, the dynamic linearization technique (DLT) is utilized to acquire an equivalent linear data model for nonlinear VPSs. Secondly, a hierarchical control structure is developed to realize the vehicular platooning tracking control task, which consists of the upper‐layer adaptive distributed observer and the under‐layer decentralized model‐free adaptive vehicular platooning tracking controller. In order to make each follower vehicle get the leader's information, the adaptive distributed observer is employed to get the estimation value of leader's information. In addition, for the purpose of ensuring the safety of driving, the radial basis function neural network (RBFNN) algorithm is utilized to address the problem of sensor failures. Based on this, a novel hierarchical distributed model‐free adaptive fault‐tolerant vehicular platooning control scheme is designed to achieve simultaneous tracking of vehicular position and speed. Lastly, the validity of the theoretical control scheme is demonstrated through a realistic and detailed simulation example of a VPS. [ABSTRACT FROM AUTHOR]

This paper presents a three-dimensional (3D) analysis of the post-buckling behavior of functionally graded (FG) plates for the first time using an elastoplasticity-based meshfree formulation. A novel 3D Tchebychev-radial point interpolation approach, which combines radial basis functions with Tchebychev polynomials, is developed to solve the nonlinear post-buckling problem. The incremental plastic deformation is modeled by the Prandtl–Reuss flow rule along the isotropic hardening von Mises criterion. The governing equations are derived using the principle of virtual work by considering the 3D full Green–Lagrange strain components. The Newton–Raphson technique along with the arc-length method is used to determine post-buckling equilibrium paths of FG plates. The effective elastoplastic properties of the functionally graded material are assessed by exploiting the homogenization method, named Tamura–Tomota–Ozawa (TTO) model. It has been demonstrated that the accuracy of the solution is improved by incorporating Tchebychev polynomials into the radial point interpolation method (RPIM) shape function, and the stability and robustness of the results are independent of variations in the shape parameter. Furthermore, it is confirmed that TRPIM, with a slightly higher CPU time compared to RPIM, exhibits a higher convergence rate. The excellent agreement of the results with those existing in the literature shows that the proposed meshfree method can be used as a robust and accurate numerical tool to predict the elastoplastic post-buckling behavior of FG plates. Further numerical assessments indicate that post-buckling paths are significantly affected by factors such as geometric parameters, material gradient, loading ratio, and boundary conditions (BCs). [ABSTRACT FROM AUTHOR]

The equalization of digital channels is widely recognized as a nonlinear classification problem. In such scenarios, utilizing networks that approximate nonlinear mappings can be highly advantageous. There has also been extensive research on equalizers based on Radial Basis Function Neural Networks (RBFNNs). This study introduces a training methodology centred on the Improved Butterfly Optimization Algorithm (IBOA) for channel equalization using RBFNN. This approach aims to optimize the performance of RBFNN equalizers by leveraging the IBOA algorithm for training. Previous literature primarily approached the equalization problem as an optimization challenge. In contrast, this study addresses it as a classification problem. This training approach exhibits substantial enhancements compared to conventional metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

In urban engineering construction, ensuring the stability and safety of subsurface geological structures is as crucial as surface planning and aesthetics. This study proposes a novel multivariate radial basis function (MRBF) interpolant for the three-dimensional (3D) modeling of engineering geological properties, constrained by the stratigraphic structural model. A key innovation is the incorporation of a well-sampled geological stratigraphical potential field (SPF) as an ancillary variable, which enhances the interpolation of geological properties in areas with sparse and uneven sampling points. The proposed MRBF method outperforms traditional interpolation techniques by showing reduced dependency on the distribution of sampling points. Furthermore, the study calculates the bearing capacity of individual pile foundations based on precise stratigraphic thicknesses, yielding more accurate results compared to conventional methods that average these values across the entire site. Additionally, the integration of 3D geological models with urban planning facilitates the development of comprehensive urban digital twins, optimizing resource management, improving decision-making processes, and contributing to the realization of smart cities through more efficient data-driven urban management strategies. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to develop a numerical model of unsaturated flow in soils with plant root water uptake. The Richards equation and different sink term formulations are used in the numerical model to describe the distribution of soil moisture in the root zone. The Kirchhoff transformed Richards equation is used and the Gardner model is considered for capillary pressure. In the proposed numerical approach, we used localized radial basis function (LRBF) meshless techniques in space and the backward Euler scheme for temporal discretization to solve the system. The LRBF approach is an accurate and computationally efficient method that does not require mesh generation and is flexible in addressing high-dimensional problems. Furthermore, this method leads to a sparse matrix system, which avoids ill-conditioning issues. We implement the numerical model of infiltration and plant root water uptake for one, two, and three-dimensional soils. Numerical experiments are performed using nontrivial analytical solutions and available experimental data to validate the performance of the proposed numerical techniques. The results demonstrate the capability of the proposed numerical model to predict soil moisture dynamics in root zone. [ABSTRACT FROM AUTHOR]

Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This study focuses on the dynamics and cooperative control for two space manipulators transporting the flexible payload. The assumed mode method is used to discretize the flexible component. Based on the Lagrange's equations of second kind and Lagrange multiplier method, the dynamics model of system is built. To compensate for the disturbances from the payload acting on the manipulators, the boundary forces and torques of the payload are estimated based on the statics analysis. A radial basis function neural network (RBF NN) is adopted to approximate some unknown terms. A NN-based cooperative controller with statics compensation is proposed for such a space manipulation system to drive the manipulators and beam to the desired states. The stability of the controller is proven through Lyapunov theory. Numerical simulations via the constant-step generalized-α integrator and some experiments based on QArm platforms are performed to show the efficiency of the designed controller. • The combination of mechanics and control theory fosters the development of flexible load transportation. • An estimation scheme of flexible structure helps to reduce the use of sensors. • Performance of the transportation with the statics compensation increases. [ABSTRACT FROM AUTHOR]