RBF Model Research Articles

Applied machine learning: Performance prediction of heat pipe with mesh wick

This study presents an investigation of a heat pipe with a mesh wick, utilizing machine learning (ML) techniques. The model including radial basis function interpolation (RBF), Kriging model (KRG), and the k-nearest neighborhood model (K-NN) were studied and compared. A set of training and validating populations were classified using a k-means clustering technique. The design variable included the geometric shape of heat pipe such as its diameter, the properties and percentage used of working fluid, and the temperature at the evaporator. The prediction case study included the heat transfer rate (q), and total difference temperature between evaporator and condenser section (ΔT). The prediction results found that the ΔT gave the most accurate indicator while the q is passable to applied. The Kriging model proved to be the most accurate, achieving an RMSE of 0.9896 and R2 of 0.9149 for heat transfer rate prediction, and an RMSE of 0.1902 and R2 of 0.9398 for total temperature difference prediction with 90 % training data. The second-best accuracy was achieved by the RBF model, with the linear, thin plate, and cubic spline kernels performing reasonably well.

Estimation Model for Maize Multi-Components Based on Hyperspectral Data.

Assessing the quality of corn seeds necessitates evaluating their water, fat, protein, and starch content. This study integrates hyperspectral imaging technology with chemometric analysis techniques to achieve non-invasive and rapid detection of multiple key components in corn seeds. Hyperspectral images of the embryo surface of maize seeds were collected within the wavelength range of 1100~2498 nm. Subsequently, image segmentation techniques were applied to extract the germ structure of the corn seeds as the region of interest. Seven spectral data preprocessing algorithms were employed, and the Detrending Transformation (DT) algorithm was identified as the optimal preprocessing method through comparative analysis using the Partial Least Squares Regression (PLSR) model. To reduce spectral redundancy and streamline the prediction model, three algorithms were employed for characteristic wavelength extraction: Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE). Using the original spectra and extracted characteristic wavelengths, PLSR, BP, RBF, and LSSVM models were constructed to detect the content of four components. The analysis indicated that the CARS-LSSVM algorithm had the best prediction performance. The PSO algorithm was employed to further optimize the parameters of the LSSVM model, thereby improving the model's prediction performance. The R values for the four components in the test set were 0.9884, 0.9490, 0.9864, and 0.9687, respectively. This indicates that hyperspectral technology combined with the DT-CARS-PSO-LSSVM algorithm can effectively detect the main component content of corn seeds. This study not only provides a scientific basis for the evaluation of corn seed quality but also opens up new avenues for the development of non-destructive testing technology in related fields.

Comparative analysis of data driven rainfall-runoff models in the Kolar river basin

To effectively tackle the challenges posed by climate change, it is crucial to enhance the accuracy of rainfall-runoff models to ensure reliability amidst changing climatic conditions. Neural networks, renowned for their ability to capture complex patterns and relationships within uncertain input and output data, offer valuable tools in this pursuit. This study aims to evaluate the efficacy of two neural network (NN) models: the Radial Basis Function Neural Network (RBFNN) and the Model Tree M5 Neural Network (MTM5NN). These models are assessed both individually and in combination with the Wavelet (WT) data processing technique for rainfall-runoff modeling in the Kolar River watershed located in Madhya Pradesh, India. Fifteen models were developed employing four algorithms: RBFNN models, WRBFNN (RBF model integrating wavelet components of rainfall as inputs), MTM5NN, and WMTM5NN (MT model incorporating wavelet components of rainfall as inputs). Initially, rainfall and runoff data underwent normalization and were applied to RBFNN and MTM5NN networks. Subsequently, time series data for both rainfall and runoff were decomposed using wavelet transforms, resulting in various sub-time series signals such as approximations and decompositions. These derived signals were then utilized as input data for RBFNN and MTM5NN, specifically designated as WRBFNN and WMTM5NN. The most effective model identified was Model 8 of WMTM5NN, which demonstrated R2 values close to 0.97, outperforming the other models. These results underscore the superior performance of the WMTM5NN model, highlighting its effectiveness in achieving heightened accuracy in predicting runoff for this specific watershed.

Enhancing slope stability prediction using a multidisciplinary approach and radial basis function neural network: A case study on the Jelapang rock slope in Perak

Accurately predicting the stability of complex high rock slopes is crucial to prevent infrastructure damage and potential casualties. Existing studies have primarily relied on linear or log-linear regression models based on laboratory data, limiting their practical application. In this study focused on the Jelapang rock slope in Perak, a multidisciplinary approach combining geological, geotechnical, and remote sensing analyses was employed to consider various rock mass properties and slope geometrical parameters. The Rock Mass Rating (RMR), Slope Mass Rating (SMR), and factor of safety (FOS) were computed for 48 regions of the rock slope. Subsequently, a Radial Basis Function Neural Network (RBFNN) model was utilised to predict the FOS, RMR, and SMR. Three RBFs were developed: RBF-1 for estimating the factor of safety, RBF-2 for rock mass rating, and RBF-3 for slope mass rating. For comparison purposes, the performance of the RBFNN models was compared with Multilayer Perceptron (MLP) models. The results demonstrated that the optimised RBFNN model provided a state-of-the-art technique for accurately predicting and controlling slope stability. Notably, the RBFNN models exhibited remarkable precision, as indicated by root-mean-squared error (RMSE) values of 2.02172e-30 for the training process of RBF-1, 7.58671e-28 for RBF-2, and 2.71129e-27 for RBF-3. Comparison results revealed that the RBF models outperformed the MLPs. Additionally, sensitivity analysis results indicated that Uniaxial Compressive Strength (UCS) had the highest effect on the FOS.

Graph Convolutional Spectral Clustering for Electricity Market Data Clustering

As the power grid undergoes transformation and the Internet’s influence grows, the electricity market is evolving towards informatization. The expanding scale of the power grid and the increasing complexity of operating conditions have generated a substantial amount of data in the power market. The traditional power marketing model is no longer suitable for the modern power market’s development trend. To tackle this challenge, this study employs random forest and RBF models for processing electricity market data. Additionally, it explores the synergy of graph convolutional network and spectral clustering algorithms to enhance the accuracy and efficiency of data mining, enabling a comprehensive analysis of data features. The experimental results successfully extracted various electricity consumption features. This approach contributes to the informatization efforts of power grid enterprises, enhances power data perception capabilities, and offers reliable support for decision makers.

A data-driven co-evolutionary exploration algorithm for computationally expensive constrained multi-objective problems

Surrogate-assisted multi-objective optimization algorithms have attracted widespread attention due to their outstanding performance in computationally expensive real-world problems. However, there is relatively little research about multi-objective optimization with complex and expensive constraints. Hence, a data-driven co-evolutionary exploration (DDCEE) algorithm is presented in this paper for the above-mentioned problems, where Radial Basis Functions are utilized to train dynamically updated surrogate models for each objective and constraint. Specifically, a data-driven co-evolutionary exploration framework is proposed to fully utilize and mine the potential available information of RBF models, and RBF models are constantly updated to guide co-evolutionary in discovering valuable feasible regions and achieving global optimization. In co-evolutionary exploration, one population focuses on exploring the entire space without considering constraints, while the other population focuses on exploring feasible regions and collaborating by sharing their respective offspring. Reference vectors are introduced in co-evolutionary exploration to divide the objective space into several sub-regions for further selection. Furthermore, an adaptive selection of promising samples strategy is presented to reasonably utilize the information of solutions with good convergence and enhance the convergence and diversity of the Pareto front. After comprehensive experiments on constrained multi/many-objective benchmark cases and an engineering application problem, DDCEE shows more stable and impressive performance when compared with five state-of-art algorithms.

Passenger Car Export Forecasting with Machine Learning: Using MLP and RBF Model

Passenger Car Export Forecasting with Machine Learning: Using MLP and RBF Model

The measurement error prediction method of voltage transformer based on TCN-RBF

Accurate prediction of voltage transformer error is an important guarantee for the stable and economic operation of the power grid. This paper presents a measurement error prediction method for voltage transformers based on time convolution and radial basis neural networks (TCN-RBF). Firstly, the ensemble empirical mode decomposition is used to decompose the original error sequence into multiple components. Secondly, a TCN prediction model was established based on each component and historical error sequence to achieve error prediction. To further improve the prediction progress, the RBF model was used to further fit the predicted results with the environmental parameter data and confirm the final predicted value. The operation error of the transformer in a substation is tested. The test results show that compared with LSTM, SVM, BP, and other prediction models, this method has better performance in error prediction.

Multi-surrogate-assisted stochastic fractal search based on scale-free network for high-dimensional expensive optimization

Surrogate-assisted meta-heuristic algorithms (SAMAs) have been increasingly popular in recent years for solving challenging optimization problems. However, the majority of recent studies concentrate on low-dimensional problems. In this paper, a scale-free network based multi-surrogate-assisted stochastic fractal search (SF-MSASFS) algorithm is proposed. Specifically, based on the stochastic fractal search (SFS) algorithm, multiple surrogate models, namely RBF and Kriging models, are used to enhance the robustness of the algorithm. The scale-free network is used to build the topology structure of the SFS algorithm, and the offspring particles are generated by means of the connection relationship between the parent particles. In addition, to further enhance adaptability, an adaptive mechanism is implemented, tailoring three distinct update mechanisms based on their corresponding reward values. Finally, the performance of the proposed algorithm is demonstrated by comparing the proposed algorithm with a number of state-of-the-art SAMAs on several well-known benchmark functions, in particular in solving high-dimensional expensive problems (HEOPs). The results underscore the SF-MSASFS algorithm’s commendable optimization performance. (The MATLAB code can be found at the authors github: https://github.com/xiaodi-Cheng/SF-MSASFS)

A novel machine learning-based imputation strategy for missing data in step-stress accelerated degradation test

The presence of missing data is a significant data quality issue that negatively impacts the accuracy and reliability of data analysis. This issue is especially relevant in the context of accelerated tests, particularly for step-stress accelerated degradation tests. While missing data can occur due to objective factors or human error, high missing rate is an inevitable pattern of missing data that will occur during the conversion process of accelerated test data. This type of missing data manifests as a degradation dataset with unequal measuring intervals. Therefore, developing a more appropriate imputation method for accelerated test data is essential. In this study, we propose a novel hybrid imputation method that combines the LSSVM and RBF models to address missing data problems. A comparison is conducted between the proposed model and various traditional and machine learning imputation methods using simulation data, to justify the advantages of the proposed model over the existing methods. Finally, the proposed model is implemented on real degradation datasets of the super-luminescent diode (SLD) to validate its performance and effectiveness in dealing with missing data in step-stress accelerated degradation test. Additionally, due to the generalizability of the proposed method, it is expected to be applicable in other scenarios with high missing data rates.

A Study on the Impact of Corporate Financial Accounting Management System on Corporate Innovation Under Sustainable Development Strategy

In the complex and changeable macro-economy, the sustainable development of enterprises is closely related to their ability to resist risks. To analyze the influencing factors of listed enterprises’ anti risk ability, this study constructs an indicator system of influencing variables through principal component analysis. The financial risk early warning method based on improved RBF neural network is constructed. Afterwards, the relevance of the indicator system was confirmed by the Kaiser-Meyer–Olkin (KMO) test. And in simulation experiments, it compared the classification performance of the BP neural network and the improved RBF model combined with the clustering algorithm. In the training dataset, the classification accuracy of the BP neural network was 79.6% while that of the improved RBF model was as high as 93.6%. In the 30 test datasets, the BP neural network appeared to have seven false positives. In the 30 test datasets, the BP neural network produced seven false judgments, while the proposed method only had three judgment errors. Several experiments showed that the BGD-RBF financial risk early warning model effectively maintained a high performance in the classification accuracy of enterprise financial status. It is more reliable than the traditional BP neural network method.

Research on Key Issues and Optimization Strategies for Emergency Response to Public Health Emergencies

Abstract The handling of every major public health event is a test of risk early warning ability and national governance capacity and will form experience and lessons in social governance. In this paper, we use the improved Apriori algorithm to mine the classification of public health emergencies, construct public health emergency response indicators, and carry out feature screening and indicator system construction. On this basis, the selected areas are analyzed using the Em prediction model based on Markov chains and Bayesian networks. In this paper, A city is selected as the research object, and the Em prediction model is first tested for its performance. By comparing it with the RBF model and the ARIMA model, the prediction model has the best accuracy, while the RBF model has the lowest accuracy. Then, the Em model was used to cluster the derived social risks in City A, and the clustering centers of four risk indices were derived, which were 0.202, 0.358, 0.492, and 0.644, respectively. Secondly, the public health of City A was graded, and the risk grades were classified into four grades: mild, moderate, severe, and extra severe. Finally, according to the classification of the level of public health event characterization, the analysis can be seen that environmental factors and plains have a greater impact on the occurrence of public health events.

Analysis of Implicit Strategy Optimization and Economic Returns of Machine Learning Techniques in Financial Asset Management

Abstract This paper formulates and adjusts investment portfolios based on machine learning techniques, adopts the B-L model after combining the GJR-GARCH-M model and the RBF model, combines the subjective view of the investor with the market equilibrium rate of return, and realizes the optimization of the implicit strategy in the management of the financial assets in the process, and finally analyzes the economic returns through the B-L model. The return of the asset portfolio under the improved B-L model is 0.37% higher than that of the market capitalization-weighted asset portfolio. The Improved B-L model’s asset allocation improves economic returns. In terms of cumulative return, the mean cumulative return of the improved B-L model is 50.7%, which is higher than the economic return of the regular B-L model and the equal-weight portfolio strategy.

Enhanced Water Quality Prediction in the Yellow River Basin: Application of the SSA-RBF Model

Watershed water quality monitoring is of great significance in water environment protection and management, and this paper proposes a water quality prediction model based on RBF neural network. Aiming at the parameter optimisation of RBF water quality prediction model, we propose to apply the sparrow search algorithm to optimise the parameters of the RBF model, aiming to improve the global search ability and convergence speed of the model. The characteristics of water quality parameters in the Shaanxi section of the Yellow River Basin were analysed using the model. Comparison with the RBF prediction algorithm is made, and the SSA-RBF prediction algorithm can significantly improve the prediction performance of the RBF model.

Residual life prediction of bearings based on RBF approximation models

Abstract Once the failure of rotating machinery occurs, it may cause the whole system to paralyze and cause great economic losses, or it may cause casualties. Therefore, the prediction of the remaining life of bearings is of great significance. The purpose of this paper is to analyze the approximate modeling technology and develop a framework for combined approximate modeling technology. A multi-strategy radial-based approximate model optimization model is proposed based on the limitations of radial-based approximate model technology. Utilizing the weight coefficient solving technique, the variable confidence RBF model, i.e., RBF-LSTM model, is established. Propose the remaining methods for life prediction using the bearing life prediction process. The RBF-LSTM combined approximation model is used to construct the evaluation index for rolling bearing remaining life prediction. Using the empirical analysis method, the optimization effects of different models and the accuracy of bearing remaining life prediction are analyzed, respectively. Experiments show that the data range of the RBF-LSTM combined approximation model is between [23,52], the overall fluctuation range of the data is not large, and the time taken is only 31 s. After 230 calculations, the model optimization effect is better. In the remaining life validation, the starting values of 132h and 148h are less different from real life, only 1.53h and 1.3h, respectively, and the model prediction accuracy is high.

Forecast earthquake precursor in the Flores Sea

<span>Artificial intelligence (AI) can use seismic training data to discover relationships between inputs and outcomes in real-world applications. Despite this, particularly when using geographical data, it has not been used to predict earthquakes in the Flores Sea. The algorithm will read the seismic data as a pattern of iterations throughout the operation. The output data is created by grouping based on clusters using the most effective WCSS analysis, while the input features are derived from the original international resource information system (IRIS) web service data. Given that earthquake prediction is an effort to reduce seismic disasters, this research is essential. By generating predictions, it can reduce the devastation caused by earthquakes. Using the support vector machine (SVM), hyperparameter support vector machine (HP-SVM), and particle swarm optimization support vector machine (PSO-SVM) algorithms, this study seeks to forecast the Flores Sea earthquake. According to the estimation results, the SVM algorithm’s evaluation value is less precise than that of the HP-SVM, especially the linear HP-SVM and HP-SVM Polynomial models. However, the HP-SVM RBF model’s accuracy rating is identical to that of the traditional SVM model. <br /> The improvement of the PSO-SVM model, which has the finest gamma position and a value of 9.</span>

Short-term solar radiation forecasting using machine learning models under different sky conditions: evaluations and comparisons.

Forecasting short-term solar radiation is crucial for many solar energy applications. Additionally, solar energy has a lower environmental impact than conventional sources like fossil fuels and can be used for investment purposes through the construction of large solar farm facilities. To test, evaluate, and compare various solar radiation models, short-term observations of meteorological, astronomical, computational, and geographical data were collected at two distinct locations from 2012 to 2015. In this study, seven machine learning models were employed: multi-layer perceptron (MLP), feedforward backpropagation algorithm (FFBP), autoregressive integrated moving average (ARIMA), linear regression (LR), radial basis function neural network (RBFNN), random forest (RF), and Gaussian process regression (GPR) models. These models were used to forecast hourly global solar radiation (GSR) using the aforementioned data as model input. The performance of the selected models' forecast accuracy was thoroughly examined by assessing it for a typical day, for four seasons, and under three sky conditions. The RF model can forecast GSR with satisfactory accuracy, and MLP and GPR models provide better accuracy than LR, FFBP, RBF, and ARIMA models. For example, the R2 value range of RF are 0.9621 for Tetuan site and 0.9534 for Tangier site, respectively. Meanwhile, RF, MLP, and GPR models under-forecast few high radiation values on clear days, which may due to the differences in training and testing data ranges and distributions of the sky conditions. Finally, the obtained result of this study indicate that the proposed RF model is a reliable alternative for short-term global solar radiation forecasting due to its high forecast accuracy.

A LATIN AMERICAN MARKET ASSET VOLATILITY ANALYSIS: A COMPARISON OF GARCH MODEL, ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR REGRESSION

The objective of this research was to compare the effectiveness of the GARCH method with machine learning techniques in predicting asset volatility in the main Latin American markets. The daily squared return was utilized as a volatility indicator, and the accuracy of the predictions was assessed using root mean square error (RMSE) and mean absolute error (MAE) metrics. The findings consistently demonstrated that the linear SVR-GARCH models outperformed other approaches, exhibiting the lowest MAE and MSE values across various assets in the test sample. Specifically, the SVRGARCH RBF model achieved the most accurate results for the IPC asset. It was observed that GARCH models tended to produce higher volatility forecasts during periods of heightened volatility due to their responsiveness to significant past changes. Consequently, this led to larger squared prediction errors for GARCH models compared to SVR models. This suggests that incorporating machine learning techniques can provide improved volatility forecasting capabilities compared to the traditional GARCH models.

Research on the Clothing Classification of the She Ethnic Group in Different Regions Based on FPA-CNN

In order to achieve the effective computer recognition of the She ethnic clothing from different regions through the extraction of color features, this paper proposes a She ethnic clothing classification method based on the Flower Pollination Algorithm-optimized color feature fusion and Convolutional Neural Network (FPA-CNN). The method consists of three main steps: color feature fusion, FPA optimization, and CNN classification. In the first step, a color histogram and color moment features, which can represent regional differences in She ethnic clothing, are extracted. Subsequently, FPA is used to perform optimal weight fusion, obtaining an optimized ratio. Kernel principal component analysis is then applied to reduce the dimensionality of the fused features, and a CNN is constructed to classify the She ethnic clothing from different regions based on the reduced fused features. The results show that the FPA-CNN method can effectively classify the She ethnic clothing from different regions, achieving an average classification accuracy of 98.38%. Compared to SVM, BP, RNN, and RBF models, the proposed method improves the accuracy by 11.49%, 7.7%, 6.49%, and 3.92%, respectively. This research provides a reference and guidance for the effective recognition of clothing through the extraction of color features.

A two-stage dominance-based surrogate-assisted evolution algorithm for high-dimensional expensive multi-objective optimization

In the past decades, surrogate-assisted evolutionary algorithms (SAEAs) have become one of the most popular methods to solve expensive multi-objective optimization problems (EMOPs). However, most existing methods focus on low-dimensional EMOPs because a large number of training samples are required to build accurate surrogate models, which is unrealistic for high-dimensional EMOPs. Therefore, this paper develops a two-stage dominance-based surrogate-assisted evolution algorithm (TSDEA) for high-dimensional EMOPs which utilizes the RBF model to approximate each objective function. First, a two-stage selection strategy is applied to select individuals for re-evaluation. Then considering the training time of the model, proposing a novel archive updating strategy to limit the number of individuals for updating. Experimental results show that the proposed algorithm has promising performance and computational efficiency compared to the state-of-the-art five SAEAs.