Kernel Function Of Support Vector Regression Research Articles

Inverse finite element method and support vector regression for automated crack detection with OFDR-Distributed fiber optic sensors

Crack detection is a crucial but challenging task. In this paper, a new method is introduced for automatical detection of the substrate cracks. An anomaly index is defined based on optical frequency domain reflectometry (OFDR) measured strain and inverse finite element method (iFEM) recovered strain. The index is sensitive to crack-induced strain but not sensitive to substrate strain. Leveraging on the self-defined anomaly index, the crack location can be identified with millimetre accuracy. Then, the detection of the crack depth is regarded as a typical multi-regression analysis and solved by support vector regression (SVR) method. Particle swarm optimization (PSO) approach is applied to find optimal parameters of the SVR kernel function. Experimental cases of a simple-supported beams with different crack locations and depths are performed, demonstrating the excellent prediction accuracy of the method. The proposed method has a promising potential in identification and quantification of cracks.

Urban river ammonia nitrogen prediction model based on improved whale optimization support vector regression mixed synchronous compression wavelet transform

Accurate prediction of ammonia nitrogen (NH3–N) in water was crucial to efficient and reasonable judgment of water quality and environmental conditions. A model named SCWT-IWOA-SVR was proposed in this study by combining synchronous compressed wavelet transform (SCWT), improved whale optimization algorithm (IWOA) and support vector regression (SVR). Firstly, the SCWT algorithm was used to de-noise the original data, overcome the nonlinearity of the data, and explore the variation rule of the water quality index time series. Statistical methods and Pearson correlation analysis were then used to select features. IWOA was introduced to optimize the hyperparameters in SVR kernel function, tent mapping was used to randomly initialize the location of the whales, making them more evenly distributed, combined wheel selection mechanism was used to select more representative prey, which leading to improve the accuracy of forecasts results. The results indicated that the SCWT-IWOA-SVR model, which incorporates synchronous compression wavelet transform and the improved whale optimization algorithm, exhibit a better prediction effect compared with SVR, WOA-SVR, WOA-SVR, and Random Forest (RF) models. The results indicated that the model has a good prediction performance of low error and high generalization (RMSE = 0.055, MAE = 0.015, R2 = 0.989), the RMSE <0.2 and R2 > 0.915 of the proposed SCWT-IWOA-SVR model in three days advance predicted. SCWT-IWOA-SVR model can provide a scientific basis for the comprehensive treatment and decision-making of water pollutants in the watershed.

Real-time anomaly data detection method based on mixed kernel function PSO-SVR

To avoid the influence of abnormal data of micro-plant factory sensors on the control system, an abnormal data detection method based on mixed kernel function particle swarm optimization (PSO)-support vector regression (SVR) is proposed. First, the mixed kernel function constructed by the polynomial kernel and radial basis kernel is used as the kernel function of SVR, and PSO is introduced to optimize the hyperparameters of the SVR model and establish a prediction model. Then, the model completes a step-by-step prediction based on the data in the sliding window and calculates confidence intervals. Finally, the data are judged to be abnormal based on whether the measured values exceed the confidence interval, and the abnormal values are replaced with the predicted values. The results show that the mean absolute percentage error of the model is 0.0063, the accuracy is 99.63%, the prediction accuracy and detection accuracy are better than the comparison model, and the abnormal data in the sensor data flow can be effectively detected and processed.

Prediction Method for Sugarcane Syrup Brix Based on Improved Support Vector Regression

The brix of syrup is an important parameter in sugar production. To accurately measure syrup brix, a novel measurement method based on support vector regression (SVR) is presented. With the resonant frequency and quality factor as inputs and syrup brix as the output, a mathematical model of the relationship between the resonant frequency, quality factor, and syrup brix is established. Simultaneously, the particle swarm optimization (PSO) algorithm is used to optimize the penalty coefficient and radial basis kernel function of SVR to improve the performance of the model. The calculation model is trained and tested using the collected experimental data. The results show that the mean absolute error, mean absolute percentage error, and root mean square error of the syrup brix calculation model based on the improved SVR model can reach 0.74 °Bx, 2.24%, and 0.90 °Bx, respectively, while the determination coefficient can reach 0.9985. The simulation of the online measurement of syrup brix in the actual production process proves the excellent prediction performance of the syrup brix calculation model based on the improved PSO–SVR model, which can thus be used to predict the syrup brix.

Control strategy research of electric vehicle thermal management system based on MGA-SVR algorithm

The thermal management system is one of the important assemblies that ensure the secure operation of electric vehicles (EVs). Using intelligent algorithms to optimize the control strategy of the thermal management system can reduce energy consumption under the premise of effective heat dissipation of EVs. This paper attempts to construct the control strategy of EV thermal management system by coupling the modified genetic algorithm (MGA) and support vector regression (SVR). Firstly, the double-population adaptive mutation method and a novel optimization process are adopted to obtain MGA. Afterward, the performance of MGA is verified by four benchmark functions compared with three typical algorithms, which are genetic algorithm (GA), double-population genetic algorithm (DPGA), and quantum genetic algorithm (QGA). The results demonstrate that the accuracy and stability of MGA are obviously better than the other three algorithms. Secondly, MGA is applied to modify parameters of SVR kernel function, and the accuracy of MGA-SVR algorithm is verified by the Auto-MPG and Computer Hardware data sets. The mean square deviations of the SVR algorithm test set are 0.0186 and 0.0806, respectively, and the mean square deviations of the MGA-SVR algorithm test set are 0.0099 and 0.0054, respectively, which fully shows that MGA-SVR have more accurate forecasting capabilities. Finally, the thermal management system model of EV is built by the one-dimensional simulation software KULI. Under the Chinese working condition, fan speed which meets the cooling requirements of the motor and controller is obtained from the KULI model, and the database is constructed. Then, MGA-SVR is trained by database and employed to predict fan speed under the Chinese working condition and obtain control strategy of the thermal management system. Compared with traditional control strategy, the thermal management system based on MGA-SVR control strategy can not only meet the radiating requirements, but also effectively reduce the power consumption of fans.

Retraction Note to: Potential of support vector regression for solar radiation prediction in Nigeria

In this paper, the accuracy of soft computing technique in solar radiation prediction based on series of measured meteorological data (monthly mean sunshine duration, monthly mean maximum and minimum temperature) taking from Iseyin meteorological station in Nigeria was examined. The process, which simulates the solar radiation with support vector regression (SVR), was constructed. The inputs were monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin) and monthly mean sunshine duration ( $$ \bar{n} $$ ). Polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to RBF. The SVR coefficient of determination R 2 with the polynomial function was 0.7395 and with the radial basis function, the R 2 was 0.5877.

A system optimisation design approach to vehicle structure under frontal impact based on SVR of optimised hybrid kernel function

Frequent occurrences of road traffic accidents put forward more and more stringent requirements on the vehicle safety performance. To optimise the vehicle structure crashworthiness, the different optimisation design methods, including deterministic, reliability-based and robust optimisation, are performed simultaneously in this study. The support vector regression (SVR) model is employed to approximate responses between design variables and objectives, and the hybrid kernel function (HKF) is introduced to overcome the drawback of a single kernel function of SVR. Meanwhile, the particle swarm optimisation (PSO) algorithm is adopted to optimise HKF–SVR model parameters and improve the accuracy of the model. By combining the nondominated Sorting Genetic Algorithm II (NSGA-II) and the Monte Carlo Simulation (MCS), the proposed optimisation design approach is proven to be an efficient and systematic tool to guarantee the reliability and robustness of the vehicle structure safety design. These different optimisation design results are discussed and contrasted with initial design. The results show that the proposed approach not only improves the crashworthiness and lightweight of vehicle, but also increases the reliability and robustness of design parameters. Through reliable and robust optimisation, more conservative solutions can be generated.

Estimation of horizontal direct solar radiation considering air quality index in China

Abstract The solar radiation is essential for multiple purposes in a wide set of human activities. Frequently direct measures can be rarely achieved via weather stations so that predictive approaches become of interest. In this paper, support vector regression (SVR) is employed to predict the daily horizontal direct solar radiation (DHDSR) considering Air Quality Index (AQI). Data from 6 locations during the period of 3 years compose the training and testing sets. For each location, 9 input parameters are considered, including the daily values for the most frequently adopted climatology parameters and air quality index (AQI). The polynomial and radial basis functions (RBF) are selected as the SVR kernel function to estimate the solar radiation. The statistical indicator of root mean square error (RMSE) ranges between 0.000363 MJ/m2 and 0.191051 MJ/m2, showing good performances of all the SVR models, while polynomial basis function based SVR is preferred for most cities due to its greater accuracy and less time consumption. Furthermore, Pearson Correlation is performed to identify the most relevant input for DHDSR prediction, which shows that sunshine duration, average relative humidity and maximum temperature are the most influential factors. The results indicate that the SVR method would be a good solution for estimation of daily horizontal direct solar radiation in China. This study provide an access to combine big data with solar radiation estimation with specific machine learning method.

Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression

The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R2) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.

RETRACTED ARTICLE: Potential of support vector regression for solar radiation prediction in Nigeria

In this paper, the accuracy of soft computing technique in solar radiation prediction based on series of measured meteorological data (monthly mean sunshine duration, monthly mean maximum and minimum temperature) taking from Iseyin meteorological station in Nigeria was examined. The process, which simulates the solar radiation with support vector regression (SVR), was constructed. The inputs were monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin) and monthly mean sunshine duration (\( \bar{n} \)). Polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to RBF. The SVR coefficient of determination R2 with the polynomial function was 0.7395 and with the radial basis function, the R2 was 0.5877.

Prediction of the solar radiation on the Earth using support vector regression technique

The solar rays on the surface of Earth is one of the major factor in water resources, environmental and agricultural modeling. The main environmental factors influencing plants growth are temperature, moisture, and solar radiation. Solar radiation is rarely obtained in weather stations; as a result, many empirical approaches have been applied to estimate it by using other parameters. In this study, a soft computing technique, named support vector regression (SVR) has been used to estimate the solar radiation. The data was collected from two synoptic stations with different climate conditions (Zahedan and Bojnurd) during the period of 5 and 7years, respectively. These data contain sunshine hours, maximum temperature, minimum temperature, average relative humidity and daily solar radiation. In this study, the polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. The performance of the proposed estimators is confirmed with the simulation results.

Protein secondary structure prediction using DWKF based on SVR-NSGAII

Prediction of protein secondary structure is an important step towards elucidating its three dimensional structure and its function. This is a challenging problem in bioinformatics. By introduction of machine learning for protein structure prediction, a solution has brought to this challenge to some extent. In the literature of Machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variable. There are endeavors to use regression methods to solve the classification problem and vice versa. To regard a classification problem as a regression one, we proposed a method which is based on Support Vector Regression (SVR) classification model as one of the powerful methods in the field of machine intelligence. We applied non-dominated Sorting Genetic Algorithm II (NSGAII) to find mapping points (MPs) for rounding a real-value to an integer one. Also NSGAII is used for finding out and tuning SVR kernel parameters optimally to enhance the performance of our model and achieve better results. At the other hand, using a suitable SVR kernel function for a particular problem can improve the prediction results remarkably but there is not a kernel which can predict all protein secondary structure classes with acceptable accuracy. Therefore we use a Dynamic Weighted Kernel Fusion (DWKF) method for fusing of three SVR kernels to achieve a supreme performance. Also to improve our method, Position Scoring Matrix (PSSM) profiles are used as the input information to it. The goals of this research are to regulate SVR parameters and fuse different SVR kernel outputs in order to determine protein secondary structure classes accurately. The obtained classification accuracies of our method are 85.79% and 84.94% on RS126 and CB513 datasets respectively and they are promising with regard to other classification methods in the literature. Moreover, for gauging our method behavior in comparison to other state of arts methods, an independent dataset is used and achieves 81.4% accuracy. Our method cannot achieve the best value for any considered performance metrics on an independent dataset but its values for whole metrics are quite acceptable.

Prediction of TVB-N content in eggs based on electronic nose

Total volatile basic nitrogen (TVB-N) content is an important freshness index of egg. An electronic nose was used to distinguish room-temperature storage periods of eggs by means of principal component analysis (PCA). The loadings plot analysis was used to identify the sensor responses as input parameters of support vector regression (SVR) model. Responses of sensor array in electronic nose were employed to establish TVB-N content model able to describe egg storage periods. Results showed that the E-nose could distinguish eggs of different storage periods by PCA. The optimum SVR kernel function was selected as Gaussian kernel by simulation. The optimum SVR inner parameters of the penalty parameter C, the radius ε and the width parameter δ2 were studied and set at 25, 2−3 and 22 by the grid searching method. The simulation results demonstrated that the SVR model could achieve better accuracy and generalization than the back-propagation neural network (BPNN). The experimental results showed that the average prediction accuracy, RMSE and MRE of the TVB-N content prediction SVR model were achieved as 94.62%, 0.09% and 0.05% respectively, which implied that the E-nose was effective for TVB-N content prediction in eggs by the SVR model.

Extremal optimization for optimizing kernel function and its parameters in support vector regression

The performance of the support vector regression (SVR) model is sensitive to the kernel type and its parameters. The determination of an appropriate kernel type and the associated parameters for SVR is a challenging research topic in the field of support vector learning. In this study, we present a novel method for simultaneous optimization of the SVR kernel function and its parameters, formulated as a mixed integer optimization problem and solved using the recently proposed heuristic ‘extremal optimization (EO)’. We present the problem formulation for the optimization of the SVR kernel and parameters, the EO-SVR algorithm, and experimental tests with five benchmark regression problems. The results of comparison with other traditional approaches show that the proposed EO-SVR method provides better generalization performance by successfully identifying the optimal SVR kernel function and its parameters.

Exploring Stock Market Dynamism in Multi-nations with Genetic Algorithm, Support Vector Regression, and Optimal Technical Analysis

in this research, an approach in combination with support vector regression (SVR), genetic algorithm (GA), and optimal technical analysis is proposed to explore stock dynamism of multi-nations under different economical environments. First, we apply full search algorithm to select the optimal number of trading days used to calculate the technical indicator values. Genetic algorithm is then used to search the best combination of parameters for SVR kernel function and technical indicators used as SVR input variables. Finally, support vector regression is then used to classify stock data based on the characteristics of non-linear classification. Also, we apply sliding windows to training data to build a steady stock exploratory approach. The data sources are stock data from four countries with different economic development degree. They include United States of America, Singapore, Taiwan, and Indonesia. In empirical results, the input variables of middle-long-term technical indicators can bring stable profits and developed country shows better efficient market.