Radial Basis Function Algorithm Research Articles

Global Stochastic Comprehensive Sensitivity Analysis Based on Robustness Grouping and Improved Pelican Algorithm-Optimized Radial Basis Function Neural Network

Abstract Modeling analysis is one of the important means to analyze practical engineering, and as technology continues to evolve, various models are getting closer and closer to reality, while at the same time, there are more and more parameters in the models. It is important to analyse the impact of these parameters on the project to assist engineers in making plans or decisions. Sensitivity analysis (SA) can describe the effect of changes in these parameters on the model. However, complex models often have dozens or even hundreds of parameters, and most current SA methods struggle to deal reliably and effectively with these high-dimensional problems. In addition, it is difficult to obtain the sensitivity of continuous points in the parameter space with traditional SA methods. Therefore, this paper proposes a method that combines adaptive grouping and an Improved Pelican Optimization Algorithm for an optimal radial basis function (IPOA-RBF) agent model to solve these problems. Firstly, a clustering grouping method considering grouping robustness is established to obtain objective and stable parameter grouping results in high-dimensional sensitivity analysis. Secondly, a proxy model based on radial basis function neural network and an improved Pelican optimization algorithm are proposed to capture the logic of the proxy model to obtain the parameter sensitivity of continuous points in the parameter space. Finally, the superiority and applicability of this method is verified using an arch dam simulation model.

Solving higher dimensional expensive black box global optimization problems using sparse directional search on surrogates

We present a new algorithm Directional Optimization Search with Surrogate (DOSS), for optimizing problems with box constraints and a computationally expensive black-box objective function. DOSS is a radial basis function (RBF) based method that mainly focuses on higher dimensional and computationally expensive objective functions that can be multimodal. DOSS introduces three new techniques not previously used in earlier RBF algorithms, including using a combination of the coordinates knowledge level, fewer initial sampling points, and the surrogate’s gradient information. Numerical results on a test set including 14 test problems with 36, 48, and 60 dimensions show that DOSS outperforms two recently published algorithms RBFOpt and TuRBO, and earlier RBF algorithms such as DYCORS. TuRBO is a Gaussian process based optimization algorithm, which outperformed earlier state-of-the-art methods. DOSS algorithm also has a good performance on real-world optimization problems, including robot pushing and rover trajectory planning problems. Almost sure convergence for the DOSS algorithm is also proven in this paper. An implementation of DOSS is available at https://doi.org/10.5281/zenodo.13731558.

Multi-body mesh deformation using a multi-level localized dual-restricted radial basis function interpolation

Multi-body dynamic problems with moving boundaries are prevalent in the computational fluid dynamics. In these problems, the original mesh is unsuitable for subsequent solution steps and is needed to be deformed. The radial basis function (RBF) interpolation is a common algorithm for the mesh deformation task. However, the multi-body mesh may contain numerous individuals and volume nodes, which will harm the computational efficiency. In order to optimize this process, we propose a new method for the mesh deformation of the multi-body configuration. In this new approach, each individual is deformed separately. Thus the global problem is decomposed into a series of local mesh deformation problems. As the computational cost to construct the interpolation system in RBF algorithm is proportional to the cube of the number of support points on the surface, converting it into multiple local mesh deformation problems can effectively reduce the CPU cost. To treat each local problem, we employ a dual-restricted RBF interpolation technique which could avoid the influence of moving individual on the other individuals. This new localized approach effectively improves the computational efficiency to construct the interpolation system but sometimes will increase the CPU cost of the mesh updating procedure. To avoid this drawback, the existed multi-level restricted RBF strategy is coupled with the new localized method to further reduce the CPU cost to update the mesh. The combination of the two techniques could enhance their advantages and avoid their drawbacks. Some numerical examples have demonstrated the abilities of the new algorithm. For instance, in the case of the three-dimensional birds flock, the CPU time to construct the interpolation system and deform the volume mesh was respectively reduced by three and two orders of magnitude compared to the global single-level method.

Innovative Integration of Computer Network Technology in Modern Educational Systems

A novel educational system has been developed using the Radial Basis Function (RBF) algorithm to address the limitations of traditional classroom environments, which often rely on standardized material and fixed teaching methods. This research assesses recent advancements in interactive intelligent education systems by combining Artificial Intelligence (AI) with interactive teaching methods. A model based on cognitive functions is constructed using the RBF algorithm to personalize instructional approaches and foster a self-directed learning platform. The research methodology involves a comprehensive literature review, developing an Education Intelligent System (EIS) using AI-driven cognitive modelling, and the implementation of the RBF algorithm within a neural network architecture. The system's effectiveness is evaluated through empirical methods, including extensive data analysis and continuous refinement based on student performance feedback. Additionally, a network topology model is designed to enhance the system's adaptability for different roles within the educational framework. The results show significant improvements in instructional effectiveness, learner engagement, and personalized learning experiences. This research demonstrates that AI and interactive technologies can revolutionize conventional educational methods, enhance learner proficiency, and cultivate a more dynamic and engaging learning environment. Future work aims to improve the system's user interface and analyze larger datasets to refine the AI algorithms further.

Adaptive Control Using Radial Basis Function Neural Networks for Pneumatic Artificial Muscle Systems

This study introduces a novel adaptive controller employing neural networks, particularly radial basis function (RBF) algorithms, to enhance the control performance of pneumatic artificial muscle (PAM)-based systems. The proposed controller seeks to address the nonlinearities and hysteresis inherent in PAM-based systems by integrating neural approximation. Experimental testing and comparisons with conventional controllers are conducted using an antagonistic configuration of PAMs. The results illustrate the precision and reliability of the proposed controller, suggesting potential for future advancements in trajectory tracking control of PAM-based systems.

Using multiple-point geostatistics for geomodeling of a vein-type gold deposit

Geostatistical cascade modeling of Mineral Resources is challenging in vein-type gold deposits. The narrow shape and long-range features of these auriferous veins, coupled with the paucity of drill-hole data, can complicate the modeling process and make the use of two-point geostatistical algorithms impractical. Instead, multiple-point geostatistics techniques can be a suitable alternative. However, the most challenging part in implementing the MPS is to use a suitable training data set or training image (TI). In this paper, we suggest using the radial basis function algorithm to build a training image and the DeeSse algorithm, one of the multiple-point statistics (MPS) methods, to model two long-range veins in a gold deposit. It is demonstrated that DeeSse can replicate long-range vein features better than plurigaussian simulation techniques when there is a lack of conditioning data. This is shown by several validation processes, such as comparing simulation results with an interpretive geological block model and replicating geological proportions.

Research on the Teaching Model of English Teaching Quality Evaluation under the Background of MOOC

In the context of informatization, online teaching is increasingly favored by people for its convenience and resource richness. Through the Open CourseWar (MOOC) platform, people can easily access learning resources and achieve efficient line length learning. However, currently MOOC online learning cannot provide targeted evaluation for the learning effectiveness of English learners, and there is relatively little research on online English quality assessment. Propose an intelligent MOOC online English teaching evaluation technology. Firstly, analyze the existing English teaching factors and construct a quality evaluation system for online English teaching using Principal Component Analysis (PCA) and expert evaluation method. Considering that the quality of English teaching is influenced by many factors, the evaluation of English teaching quality belongs to a complex nonlinear solving problem. Therefore, the advanced GA-RBF (Genetic Algorithm Radial Basis Function Neural Network, GA-RBF) model is adopted to solve the English teaching quality evaluation model. In teaching quality evaluation, BP and RBF are selected to participate in comparative testing of teaching quality evaluation. In the training loss test of multiple models, the GA-RBF model has the best convergence speed and training performance in the oral sample test, tends to converge after 270 iterations, with a loss value of 0.24. In the evaluation of English proficiency indicators, the BP model has a significant error in testing, with an error of 13 in the evaluation of reading ability scores. The error of RBF in reading ability score evaluation is 4, and the GA-RBF model performs the best. The reading ability evaluation error is 1, and the overall evaluation performance is the best. Through the above research, an intelligent method for evaluating the quality of English teaching is proposed, which will provide important technical references for MOOC online education evaluation and English teaching improvement.

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and provide valuable insights to investors and financial analysts. The MLP algorithm, based on artificial neural networks, is able to learn complex patterns and relationships within financial data. The RBF algorithm, on the other hand, utilizes radial basis functions to capture non-linear relationships and identify hidden patterns within the data. By combining these algorithms, we aim to enhance the accuracy of stock price prediction and classification models. The results showed that both MLB and RBF predicted stock prices well for a group of countries using an index reflecting the impact of news on economic policy and expectations, where the MLB algorithm proved its ability to predict chain data. Countries were also classified according to stock price data and uncertainty in economic policy, allowing us to determine the best country to invest in according to the data. The uncertainty surrounding economic policy is what makes stock price forecasting so crucial. Investors must consider the degree of economic policy uncertainty and how it affects asset prices when deciding how to allocate their assets.

A study of angle parametric optimization of the composite honeycomb on turbine blade tip

To reduce the secondary flow loss of the high-pressure turbine caused by the tip leakage flow, the design of the composite honeycomb tip has been experimentally and numerically examined in this study. The arrangement angle of the composite honeycomb is defined and optimized using the radial basis function and genetic algorithm. The tip flow field of the high-pressure turbine blade with a 2.128 mm clearance is simulated by CFX 18.0, and the arrangement angle of the composite honeycomb is optimized to obtain a lower loss. Moreover, the flat tip, basic honeycomb tip, and optimized honeycomb tip are tested in a low-speed cascade test facility. The results show that the vortices and radial velocity induced by the honeycomb structure cavity increase the kinetic energy loss of the leakage flow and reduce the leakage flow rate. Compared with the basic honeycomb, the optimized honeycomb has a supplemental blocking effect on the tip leakage flow by reducing the crosswise pressure gradient in the blade tip. The optimized honeycomb also changes the outlet angle of the leakage flow in the tip clearance, reduces the leakage vortex, and the total pressure loss is further reduced by 2.5%.

A comparative study of different radial basis function interpolation algorithms in the reconstruction and path planning of γ radiation fields

Accurate reconstruction of radiation field and path planning are very important for the safety of operators in the process of dismantling nuclear facilities. Based on radial basis function (RBF) interpolation algorithm, this paper discussed the application of inverse multiquadric radial basis Function (IMRBF) interpolation method to the reconstruction of gamma radiation field, and proved the feasibility of reconstructing a radiation field with multiple γ sources. The average relative errors of IMRBF interpolation results were 4.28% and 8.76%, respectively, for the experimental scenarios with single and double gamma sources. After comparing the consistency between the simulated scene and the experimental scene, IMRBF method and Cubic Spline method were respectively used to reconstruct the gamma radiation field by Geant4 simulation data. The results showed that the interpolation accuracy of IMRBF method was superior to that of Cubic Spline method. Further, more RBF interpolation algorithms were used to reconstruct the multi-γ source radiation field, and then the Probabilistic Roadmap (PRM) algorithm was used to optimize the human walking path in the radiation field reconstructed by different interpolation methods. The optimal paths in radiation fields generated by multiple interpolation methods were compared. The results herein contribute to a comprehensive understanding of RBF interpolation methods in reconstructing γ radiation fields and their application in optimizing paths in radiation environments. The insights may provide valuable information for decision-making in radiation protection during the decommissioning of nuclear facilities.

Classification of cancer types based on microRNA expression using a hybrid radial basis function and particle swarm optimization algorithm.

The diagnosis and treatment of cancer is one of the most challenging aspects of the medical profession, despite advances in disease diagnosis. MicroRNAs are small noncoding RNA molecules involved in regulating gene expression and are associated with several cancer types. Therefore, the analysis of microRNA data has become one of the most important areas of cancer research in recent years. This paper presents an improved method for cancer-type classification based on microRNA expression data using a hybrid radial basis function (RBF) and particle swarm optimization (PSO) algorithm. Two datasets containing microRNA information were used, and preprocessing and normalization operations were performed on the raw data. Feature selection was carried out by using the PSO algorithm, which can identify the most relevant and informative features in the data along with helping to prioritize them. Using a PSO algorithm for feature selection is an effective approach to microRNA analysis. This enhances the accuracy and reliability of cancer-type classifications based on microRNA expression data. In the proposed method, we, respectively, achieved an accuracy of 0.95% and 0.91% on both datasets, with an average of 0.93%, using an improved RBF neural network classifier. These results demonstrate that the proposed method outperforms previous works. RESEARCH HIGHLIGHTS: To enhance the accuracy of cancer-type classifications based on microRNA expression data. We present a minimal feature selection method using particle swarm optimization to reduce computational load & radial basis function to improve accuracy.

Prediction method for response characteristics parameters of isolated-span overhead lines after ice-shedding based on finite element simulation and machine learning

In response to the problem of insufficient research on the prediction for response characteristics parameters of isolated-span transmission lines under various ice-shedding modes, study on the prediction for ice-shedding response parameters of isolated-span overhead lines is conducted in this paper based on finite element simulation and machine learning. Firstly, the finite element simulation model of isolated-span overhead line is established and verified. Seven model input parameters are determined by considering 12 typical ice-shedding modes and ice-shedding labels are introduced. Six key output parameters reflecting the electrical and mechanical characteristics of the ice-shedding transmission line are obtained by feature extraction. Secondly, the output parameters of the ice-shedding transmission line under different input parameter conditions are obtained by using the finite element simulation method. A total of 1400 sets sample data are constructed. Then, four typical regression prediction models of BP (Back Propagation Algorithm), RBF (Radial Basis Function Algorithm), SVM (Support Vector Machine Algorithm) and RF (Random Forest Algorithm) are established to predict the key output parameters of ice-shedding transmission lines. Finally, the optimal prediction model for ice-shedding response characteristics parameters of isolated-span transmission lines is selected and the application effect is analyzed by actual ice-shedding case. It is found that the RF prediction model is the optimal prediction model among the four regression prediction models. The prediction method for ice-shedding response characteristics parameters of isolated-span transmission line proposed in this paper can accurately predict the response characteristics parameters of ice-shedding transmission lines which can provide some references for the safe operation of isolated-span transmission lines in heavy ice areas.

Pantograph–Catenary Interaction Prediction Model Based on SCSA-RBF Network

As a traditional numerical simulation method for pantograph–catenary interaction research, the pantograph–catenary finite element model cannot be applied to the real-time monitoring of pantograph–catenary contact force, and the computational cost required for the multi-parameter joint optimization of the pantograph–catenary system with the finite element model is very high. In this paper, based on the selective crow search algorithm–radial basis function (SCSA-RBF) network, the time-domain signal of the panhead acceleration, which can be obtained in real-time through non-contact test technology, is taken as the boundary condition to directly solve the pantograph dynamic equation and a data-physics coupling model that can quickly predict the pantograph–catenary interaction is proposed. The prediction model is trained and verified using the dataset generated through the finite element model. Furthermore, the prediction model is applied to the multi-parameter joint optimization of six pantograph dynamic parameters and nine pantograph dynamic parameters, considering nonlinear panhead stiffness, and optimization suggestions under various speeds and filtering frequencies are given.

Design of image intelligent focusing system based on improved SMD function and RBF algorithm.

The utilization of digital statistical processes in images and videos can effectively tackle numerous challenges encountered in optical sensors. This research endeavors to overcome the limitations inherent in traditional focus models, particularly their inadequate accuracy. It aims to bolster the precision of real-time perception and dynamic control by employing enhanced data fusion methodologies. The ultimate objective is to facilitate information services that enable seamless interaction and profound integration between computational and physical processes within an open environment. To achieve this, an enhanced sum-modulus difference (SMD) evaluation function has been proposed. This innovation is founded on the concept of threshold value evaluation, aimed at rectifying the accuracy shortcomings of traditional focusing models. Through the computation of each gray value after threshold segmentation, the method identifies the most suitable threshold for image segmentation. This identified threshold is then applied to the focus search strategy employing the radial basis function (RBF) algorithm. Furthermore, an intelligent focusing system has been developed on the Zynq development platform, encompassing both hardware design and software program development. The test results affirm that the focusing model based on the improved SMD evaluation function rapidly identifies the peak point of the gray variance curve, ascertains the optimal focal plane position, and notably enhances the sensitivity of the focusing model.

Face orientation recognition based on Kinectv2 and an improved ELM model

Abstract Face orientation recognition is a crucial component of human behavior analysis. Challenges such as face occlusion and varying lighting conditions frequently degrade recognition accuracy. This study introduces an innovative approach using the Kinect sensor’s skeleton tracking technology to capture face orientation images across different lighting scenarios. To enhance image quality, techniques such as light attenuation and compensation are applied. Furthermore, multiple feature vectors based on face orientation are extracted and fused, followed by the optimization of the Extreme Learning Machine (ELM) model through the Archimedean Optimization Algorithm (AOA). The developed AOA-ELM model for face orientation recognition is compared with traditional models, including Back Propagation (BP), Radial Basis Function (RBF), standard ELM, and Genetic Algorithm enhanced ELM (GA-ELM). The AOA-ELM model exhibits superior performance in estimating face orientation amidst lighting disturbances, achieving an accuracy exceeding 95% in varied lighting conditions, thereby outperforming other classification models.

Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling

For conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (φ) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap between experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero.

Agile and integrated workflow proposal for optimising energy use, solar and wind energy potential, and structural stability of high-rise buildings in early design decisions

Postponing performance analysis of buildings until the final stage of the design process often leads to unexpected results, introducing errors and disrupting the architect's workflow. The literature review identifies prior research limitations, motivating this study to develop a high-rise design optimisation workflow, automate design preparation, explore alternative airflow simulation methods, and compare Optimisation (Opt) algorithms with Genetic Algorithms (GAs) for broader design considerations. We thus propose an integrated computational workflow for sustainable building design optimisation with four objectives. This study applied it in the early design phase of a practical energy efficiency-oriented redevelopment project involving a cluster of seven high-rise office buildings in Nanjing. The project aimed to decrease annual energy use, increase solar and wind energy potential, and reduce structural displacement. Our methodology primarily utilises the surrogate-model algorithm Radial Basis Function Muti-objective Optimisation (RBFMOpt) algorithm, enabling quick identification of self- and new- energy demands and cost-effective structural solutions for tall buildings while optimising energy performance. Different from traditional genetic algorithm-driven optimisation procedures or with a separated post-optimisation, our workflow follows a pre-design approach. Test results are automatically optimised in a loop without post-processing, aligning with the architect's research objectives and improving the decision-making process. Our study yielded a 12% increase in energy use efficiency and a 23% improvement in structural stability. However, achieving these gains resulted in a 32% decrease in solar energy potential. Furthermore, we provide a comparative study between RBFMOpt and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), offering critical guidance for designers when selecting optimisation algorithms to address efficiency and precision challenges in energy-related design. These findings contribute to an integrated research-for-design workflow, providing data-driven evidence to support early design decisions.

A rapid method to predict biaxial fatigue life of automotive wheels using proper orthogonal decomposition and radial basis function algorithm

This paper presents a rapid method for predicting the biaxial fatigue life of automotive wheels using a combination of proper orthogonal decomposition and radial basis function algorithm. Currently, numerical simulations of biaxial fatigue tests are being developed to evaluate wheel performance. However, these simulations are computationally expensive due to the need to simulate multiple discrete loading cases within a given biaxial spectrum. To address this issue, we propose a novel approach that utilizes proper orthogonal decomposition and radial basis function algorithm to improve computational efficiency. By leveraging high-fidelity simulation results from a small number of loading cases, a reduced order model is constructed to accurately predict the tire-rim interface force fields required for wheel strength calculations. The reduced order model significantly reduces the computational time by 65.4% for simulating all loading cases, while maintaining a maximum predicted error of less than 2% compared to the high-fidelity model. Subsequently, the predicted interface forces are mapped onto the rim surface for strength calculation, and the wheel fatigue life is determined using the Brown-Miller multiaxial damage criterion. Comparative analysis with experimental results demonstrates the desirable accuracy of our method in simulating the stress-strain history, crack initiation position, and minimum fatigue life of the wheel. Overall, the proposed method offers a powerful tool for the rapid fatigue analysis of spectrum-loaded wheels, providing an efficient and accurate means of predicting biaxial fatigue life.

Artificial neural network based prediction and optimization of centelloside content in Centella asiatica: A comparison between multilayer perceptron (MLP) and radial basis function (RBF) algorithms for soil and climatic parameter

Artificial neural network based prediction and optimization of centelloside content in Centella asiatica: A comparison between multilayer perceptron (MLP) and radial basis function (RBF) algorithms for soil and climatic parameter

Research on the Adaptability of Typical Denoising Algorithms Based on ICESat-2 Data

Photon-counting light detection and ranging (LiDAR) emits and receives weak photon signals, which are easily mixed with background noise caused by the sun, the atmosphere, etc., and is thus difficult to distinguish. Therefore, point-cloud denoising is a key step in point-cloud data processing of photon-counting LiDAR. To explore the adaptability of different denoising algorithms for photon-counting LiDAR data in different times and spaces, in this paper, NASA’s official differential, regressive and Gaussian adaptive nearest neighbor (DRAGANN) algorithm; Herzfeld’s radial basis function (RBF) denoising algorithm; and the density-based spatial clustering of applications with noise (DBSCAN) algorithm based on density clustering are used to denoise the ICESat-2 ATL03 photon point-cloud data. Airborne LiDAR data are used to verify the denoising accuracy, and then the adaptability of the three algorithms is discussed. The results show that the DRAGANN algorithm is suitable for data with moderate Fraction Vegetation Coverage (FVC) (45–75%) at night and is less affected by slope; therefore, it is not limited to terrain slope. The denoising accuracy of the RBF algorithm decreases with increasing FVC and decreases with increasing slope. It is suitable for data with low terrain slope (0~55°) and low FVC (0~220°), which is less affected by observation time; therefore, it is suitable for all-day data. The DBSCAN algorithm is suitable for data with moderate FVC (45~75%) at night, regardless of terrain slope. Unlike the DRAGANN algorithm, the DBSCAN algorithm is greatly affected by solar noise photons, but at night, its denoising accuracy is higher than that of the DRAGANN algorithm. The research results have certain reference significance for the subsequent processing and application of ICESat-2 data.