Parameters Of Support Vector Regression Research Articles

Machine learning based system identification of a realistic heat integrated distillation column using particle swarm optimization

In the petrochemical industry, heat integration is one of the most commonly used methods for saving energy in the distillation column. The component of the liquid mixture can be estimated by correctly modeling or identifying the respective heat integrated distillation column (HIDC). Previously, system identification for HIDC has been performed using steady state data in an open loop configuration. However, the data obtained from steady state does not characterize realistic HIDC. Complex nonlinear modeling is required to account for the nonlinearities present in HIDC. In this study, a unique approach is presented to identify a dynamic closed-loop HIDC system using Support Vector Regression (SVR). Here, HYSYS is used to simulate HIDC in a fairly realistic environment and the resulting data is used in MATLAB for system identification. Particle Swarm Optimization (PSO) is used to optimize the SVR parameters, which gives better results than conventional metaheuristic optimization approaches. The proposed approach is also verified by accurately estimating the mole fraction of benzene-toluene mixtures in a dynamic HIDC. In terms of root mean square error (RMSE) and regression coefficients (R), the proposed strategy outperforms artificial neural networks (ANN) and radial basis function (RBF) models. The proposed technique reduces the RMSE by 80% and 47% compared to ANN for the mole fractions of benzene and toluene, respectively.

Parameter Optimization of Support Vector Machine to Improve the Predictive Performance for Determination of Aflatoxin B1 in Peanuts by Olfactory Visualization Technique.

This study proposes a novel method for detection of aflatoxin B1 (AFB1) in peanuts using olfactory visualization technique. First, 12 kinds of chemical dyes were selected to prepare a colorimetric sensor to assemble olfactory visualization system, which was used to collect the odor characteristic information of peanut samples. Then, genetic algorithm (GA) with back propagation neural network (BPNN) as the regressor was used to optimize the color component of the preprocessed sensor feature image. Support vector regression (SVR) quantitative analysis model was constructed by using the optimized combination of characteristic color components to achieve determination of the AFB1 in peanuts. In this process, the optimization performance of grid search (GS) algorithm and sparrow search algorithm (SSA) on SVR parameter was compared. Compared with GS-SVR model, the model performance of SSA-SVR was better. The results showed that the SSA-SVR model with the combination of seven characteristic color components obtained the best prediction effect. Its correlation coefficients of prediction (RP) reached 0.91. The root mean square error of prediction (RMSEP) was 5.7 μg·kg−1, and ratio performance deviation (RPD) value was 2.4. The results indicate that it is reliable to use the colorimetric sensor array with strong specificity for the determination of the AFB1 in peanuts. In addition, it is necessary to properly optimize the parameters of the prediction model, which can obviously improve the generalization performance of the multivariable model.

Traffic flow forecasting using natural selection based hybrid Bald Eagle Search-Grey Wolf optimization algorithm.

In a fast-moving world, transportation consumes most of the time and resources. Traffic prediction has become a thrust application for machine learning algorithms to overcome the hurdles faced by congestion. Its accuracy determines the selection and existence of machine learning algorithms. The accuracy of such an algorithm is improved better by the proper tuning of the parameters. Support Vector Regression (SVR) is a well-known prediction mechanism. This paper exploits the Hybrid Grey Wolf Optimization–Bald Eagle Search (GWO-BES) algorithm for tuning SVR parameters, wherein the GWO selection methods are of natural selection. SVR-GWO-BES with natural selection has error performance increases by 48% in Mean Absolute Percentage Error and Root Mean Square Error, with the help of Caltrans Performance Measurement System (PeMS) open-source data and Chennai city traffic data for traffic forecasting. It is also shown that the increasing population of search agents increases the performance.

Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models.

Hemoglobin is one of the most important blood elements, and its optical properties will determine all other optical properties of human blood. Since the refractive index (RI) of hemoglobin plays a vital role as a non-invasive indicator of some illnesses, accurate calculation of it would be of great importance. Moreover, measurement of the RI of hemoglobin in the laboratory is time-consuming and expensive; thus, developing a smart approach to estimate this parameter is necessary. In this research, four viable strategies were used to make a quantitative correlation between the RI of hemoglobin and its influencing parameters including the concentration, wavelength, and temperature. First, alternating conditional expectations (ACE), a statistical approach, was employed to generate a correlation to predict the RI of hemoglobin. Then, three different optimized intelligent techniques—optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)—were used to model the RI. A bat-inspired (BA) algorithm was embedded in the formulation of intelligent models to obtain the optimal values of weights and biases of an artificial neural network, membership functions of the fuzzy inference system, and free parameters of support vector regression. The coefficient of determination, root-mean-square error, average absolute relative error, and symmetric mean absolute percentage error for each of the ACE, ONN, OFIS, and OSVR were found as the measure of each model’s accuracy. Results showed that ACE and optimized models (ONN, OFIS, and OSVR) have promising results in the estimation of hemoglobin’s RI. Collectively, ACE outperformed ONN, OFIS, and OSVR, while sensitivity analysis indicated that the concentration, wavelength, and, lastly, temperature would have the highest impact on the RI.

Prediction of compressive strength of masonry structures: Integrating three optimized models by virtue of committee machine

An accurate computation of the compressive strength of masonry structures is an overarching factor in design and construction of masonry structures. This considerable significance compels researchers to propose an appropriate, reliable and, more generalized method whereby the precise value of compressive strength of masonry structures is calculated. In the current study, a committee machine with optimized elements is constructed, thereby extracting a non-linear relationship between compressive strength of masonry structures with compressive strength of mortar and brick. In order to accomplish this objective, three intelligent models viz. neural network, fuzzy inference system, and support vector regression are firstly optimized with bat-inspired algorithm, and these improved models are subsequently applied for estimation of compressive strength of masonry structures. bat-inspired algorithm is hybridized with intelligent models for extracting the best values of weights and biases of neural network, membership’s functions of fuzzy inference system, and user-defined parameters of support vector regression. Then, committee machine is utilized for amalgamating the outputs of three optimized models incl. optimized neural network, optimized fuzzy inference system, and optimized support vector regression. bat-inspired algorithm is also embedded in the structure of committee machine, thereby determining the optimal contribution of each optimized model in the final prediction. Data sets including 96 records of accessible in the literature are used to learn and evaluate the constructed models. Appraisal of the accuracy based on statistical parameters verified that the committee machine could effectively improve the prediction accuracy of the optimized models and also has a better performance compared to commonly well-known predictive correlations. This study also proved that committee machine with optimized elements is a very convenient approach for mapping nonlinear functions between compressive strength of masonry structures and compressive strength of brick and mortar.

Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR

In order to evaluate the effect of laser cleaning, a new method of workpiece surface roughness estimation is proposed. First, a Cartesian robot and visible-light camera are used to collect a large number of surface images of a workpiece after laser cleaning. Second, various features including the Tamura coarseness, Alexnet abstract depth, single blind/referenceless image spatial quality evaluator (BRISQUE), and enhanced gray level co-occurrence matrix (EGLCM) are computed from the images above. Third, the improved particle swarm optimization (IPSO) is used to improve the training parameters of support vector regression (SVR). The learning factor of SVR adopts the strategy of dynamic nonlinear asynchronous adaptive adjustment to improve its optimization-processing ability. Finally, both the image features and the IPSO-SVR are considered for the surface roughness estimation. Extensive experiment results show that the accuracy of the IPSO-SVR surface roughness estimation model can reach 92.0%.

Preserved anatomical bypasses predict variance in language functions after stroke

The severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain.Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than the most reliably observed connections in age-matched control networks. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a principal components analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using support vector regression, a technique that extracts robust relationships in high-dimensional data analysis. The support vector regression parameters were tuned using grid-search cross-validation.We discovered that the length of bypasses reliably predicted variance in lexical production (R2 = .576) and auditory comprehension scores (R2 = .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R2 = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.

Point Kernel Modification Including Support Vector Regression Neutron Buildup Factor Models

This work presents the results of radiation shielding calculations using modified point kernel code QAD-CGGP. The modification includes a new approach to neutron buildup factor estimations based on machine learning technique called Support vector regression (SVR). SVR neutron buildup factor models for common shielding materials are developed and built into the QAD-CGGP. The development of the models consisted of acquiring the data to be used for learning the model, optimizing the SVR parameters, and application of active learning methods for improving the learning process. The modified code is tested, and the results are compared with the MCNP6 results.

A traffic flow forecasting method based on the GA-SVR

This paper uses support vector regression (SVR) to predict short-term traffic flow, and studies the feasibility of SVR in short-term traffic flow prediction. The short-time traffic flow has many influencing factors, which are characterized by nonlinearity, randomness and periodicity. Therefore, SVR algorithm has advantages in dealing with such problems. In order to improve the prediction accuracy of the SVR, this paper uses genetic algorithm (GA) to optimize the SVR and other parameters to obtain the global optimal solution. The optimal parameters are used to construct the SVR prediction model. This paper selects the traffic flow data of the Jiangxi Provincial Transportation Department database to verify the feasibility and effectiveness of the proposed model.

Estimation of the longitudinal dispersion coefficient via a fusion of optimized models

Abstract Determination of the longitudinal dispersion coefficient (LDC) is fundamental to the development of strategies for environmental management of river systems. This paper presents an integrated model for an estimation of the longitudinal dispersion coefficient by a fusion of optimized intelligent models (optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)) via committee machine (CM), with optimization done by the Bat-inspired algorithm (BA). The optimization eliminates the associated loss of accuracy of the intelligent models, which is a direct consequence of an improper adjustment of parameters (weights and biases in the neural network, membership's functions in the fuzzy inference system, and user-defined parameters in support vector regression). Data gathered from literature are employed to validate the proposed integrated model. A comparison between the optimized models and a committee machine, based on statistical parameters, shows that the committee machine model can attain high accuracy. Sensitivity analysis (SA) shows the contribution of each optimized model to the committee machine and ranks the contribution of the optimized models in ascending order as optimized neural network, optimized fuzzy inference system, and optimized support vector regression, each significantly correlated with the accuracy of longitudinal dispersion coefficient prediction.

Economic Indicators Selection for Property Crime Rates using Grey Relational Analysis and Support Vector Regression

Features selection is very important in the multivariate models because the accuracy of forecasting results produced by the model are highly dependent on these selected features. The purpose of this study is to propose grey relational analysis and support vector regression for features selection. The features are economic indicators that are used to forecast property crime rate. Grey relational analysis selects the best data series to represent each economic indicator and rank the economic indicators according to its importance to the property crime rate. Next, the support vector regression is used to select the significant economic indicators where particle swarm optimization estimates the parameters of support vector regression. In this study, we use unemployment rate, consumer price index, gross domestic product and consumer sentiment index as the economic indicators, as well as property crime rate for the United States. From our experiments, we found that the gross domestic product, unemployment rate and consumer price index are the most influential economic indicators. The proposed method is also found to produce better forecasting accuracy as compared to multiple linear regressions.

Optimized Support Vector Regression for Predicting Leishmaniasis Incidences

Support Vector Regression (SVR) is a new approach in machine learning for time series prediction showing good performance. A big challenge for achieving optimal accuracy is the choice of appropriate parameters. In this paper, a Novel Enhanced Differential Evolution (NEDE) algorithm is proposed to calculate the optimal SVR parameters, and the combination approach (NEDE-SVR) was applied to predict the incidences of Zoonotic Cutaneous Leishmaniasis (ZCL) diseases. The NEDE-SVR based prediction model incorporates the climate factors as predictor variables, determined by analyzing their time lags related to the ZCL incidence. Conducted experiments have shown that NEDE-SVR exhibits good competitive performance using past diseases and climate data to predict the future cases of the ZCL disease. Accurate and timely ZCL disease predictions could aid structure health responses by informing key preparation and mitigation efforts.

An optimized support vector regression for prediction of bearing degradation

Machine learning and deep learning models are gradually being applied to predict the remaining useful life of bearings. But, still extracting effective features from the bearing signals and enhancing the prediction accuracy is a major concern. Therefore, an optimized support vector regression (SVR) model for prediction of bearing degradation is proposed. Firstly, the original time domain and frequency domain features extracted from the bearing vibration signal are learned by using the deep neural network (DNN) to improve the quality of degradation features. Secondly, a novel multi-population fruit fly optimization algorithm (MPFOA) is proposed by introducing multi-population mechanism. Thirdly, MPFOA is employed to choose the parameters of SVR, then we use MPFOA-SVR to predict the bearing remaining useful life through the degradation features learned by DNN. At last, CEC 2017 unconstrained benchmark functions and a real bearing dataset (IEEE 2012 PHM) are used to verify the performance of MPFOA and optimized SVR models respectively. Numerical experimental results show that MPFOA has a better optimization ability than the compared meta-heuristic algorithms. The optimized SVR model has a higher prediction accuracy in predicting the bearing remaining useful life.

Penerapan Algoritma Support Vector Regression untuk Prediksi Jumlah Pasien Covid-19 di Provinsi Riau

In 2019, at the end of December, there was an outbreak of a disease with an unknown cause in Wuhan, Hubei Province, China. The World Health Organization has named the outbreak of the disease as coronavirus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or Covid-19. Covid-19 is a disease outbreak that has spread in various regions of Indonesia, such as in Riau, at PT. Nusantara V Plantation (PTPN V). So we need a way to increase awareness and vigilance, namely by presenting information using Data Mining in predicting the number of cases with thealgorithm Support Vector Regression (SVR). The prediction process is carried out using SVR by specifying the SVR and Kernel Linear parameters. The SVR algorithm can predict the number of Covid-19 patients in the next 30 days so that the Correlation Coefficienti (R) level is 85% and the Mean Square Error (MSE) value is 0.196. From the results of the experiment, there was a decrease in cases of Covid-19 patients at PT. Perkebunan Nusantara V in the next 30 days, with the acquisition of the best minimum sensitivity value of 0.09

System identification and control of heat integrated distillation column using artificial bee colony based support vector regression

Distillation is a high-energy process widely employed in separating fluid mixtures in the oil and gas industries. Heat integration is one of the practical approaches for energy saving in the distillation columns. Proper identification or modeling of heat-integrated distillation column (HIDC) is employed to predict the composition of fluid mixtures. The nonlinear modeling of HIDC is highly challenging, and methods based on the first principles are not effective in coping with the nonlinearities. Hence, a novel, non-parametric support vector regression (SVR) approach is proposed for system identification and control of HIDC in this work. SVR parameters were optimized using artificial bee colony (ABC) algorithm, which resulted in better performance over other meta-heuristic algorithms. Moreover, the SVR model demonstrated better performance than the artificial neural network models in root mean square error (RMSE) and regression coefficient (R). RMSE and R values for ABC-SVR were found to be 0.0010 and 0.99992, respectively, with the validation dataset. The performance of the SVR and PID controllers are also compared. Integral square error (ISE), integral average error (IAE), integral time square error (ITSE), and integral time average error (ITAE) are the comparison metrics employed, which yielded minimal values of and respectively, for the SVR controller. The proposed model outperforms all other related methods, and it can be used to predict the mole fraction of Benzene in Benzene-Toluene HIDC accurately.

RETRACTED ARTICLE: Groundwater pollution prevention based on improved particle swarm algorithm and sports training optimization

In this paper, an improved particle swarm optimization algorithm based on chaos adaptive strategy of support vector regression parameters is proposed. Chaos mapping algorithm and adaptive aggregation decision-making strategy are used to improve the general research results of population superiority, improve the diversity of particles, and avoid the population approaching in advance. Applying better algorithm to particle swarm optimization plays an important role in preventing groundwater pollution and optimizing motion training. As a part of the prevention and control of groundwater pollution, we collected 60 groundwater quality reports and analyzed and introduced them in detail by using the evaluation method of groundwater indicators. The results of groundwater quality evaluation by pollution index method show that most of the groundwater quality in this area has been damaged, and the normal implementation of pollution prevention and control can be ensured by evaluating individual or seriously polluted water bodies, which plays the same role in the optimization of sports training. In the process of research, this paper adopts a variety of methods to investigate it, including case study method and questionnaire interview method. From the perspective of practice at home and abroad, this paper makes an in-depth analysis of the optimization methods of sports training. With the cooperation of the sports production team, we are studying the optimization index of sports training every day and implementing the training plan to track the modeling of sports training. We regularly check the optimized design of the sports training system. Finally, it is concluded that optimizing the design of college physical education teaching method system is an effective way of scientific research benefit. This paper studies the prevention and control of groundwater pollution and the optimization of sports training, and applies it to the improved particle swarm optimization algorithm to promote its application in real life.

Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization

Short-term electric load forecasting plays a significant role in the safe and stable operation of the power system and power market transactions. In recent years, with the development of new energy sources, more and more sources have been integrated into the grid. This has posed a serious challenge to short-term electric load forecasting. Focusing on load series with non-linear and time-varying characteristics, an approach to short-term electric load forecasting using a “decomposition and ensemble” framework is proposed in this paper. The method is verified using hourly load data from Oslo and the surrounding areas of Norway. First, the load series is decomposed into five components by variational mode decomposition (VMD). Second, a support vector regression (SVR) forecasting model is established for the five components to predict the electric load components, and the grey wolf optimization (GWO) algorithm is used to optimize the cost and gamma parameters of SVR. Finally, the predicted values of the five components are superimposed to obtain the final electric load forecasting results. In this paper, the proposed method is compared with GWO-SVR without modal decomposition and using empirical mode decomposition (EMD) to test the impact of VMD on prediction. This paper also compares the proposed method with the SVR model using VMD and other optimization algorithms. The four evaluation indexes of the proposed method are optimal: MAE is 71.65 MW, MAPE is 1.41%, MSE is 10,461.32, and R2 is 0.9834. This indicates that the proposed method has a good application prospect for short-term electric load forecasting.

Intelligent Approach for Enhancing Prediction Issues in Scalable Data Mining

Support vector regression (SVR) is one of the supervised machine learning algorithms that can be exploited for prediction issues. The main enhancement issue of SVR is attempting to select a reliable parameter to assure the high performance of SVR. In this paper, the intelligent approach is based on integrating the enhanced particle swarm optimization PSO with the SVR to achieve the proper SVR parameters that are used to improve SVR performance. The enhanced PSO is performed by implementing parallelized linear time-variant acceleration coefficients (TVAC) and inertia weight (IW) of PSO, called PLTVACIW-PSO. The proposed approach is evaluated by performing the experimental comparisons of the proposed algorithm with eleven different algorithms. These comparisons are performed by applying the proposed algorithm and these algorithms to 21 different datasets varying in their scales.

An Effective Hybrid Approach for Forecasting Currency Exchange Rates

Accurately forecasting the movement of exchange rates is of interest in a variety of fields, such as international business, financial management, and monetary policy, though this is not an easy task due to dramatic fluctuations caused by political and economic events. In this study, we develop a new forecasting approach referred to as FSPSOSVR, which is able to accurately predict exchange rates by combining particle swarm optimization (PSO), random forest feature selection, and support vector regression (SVR). PSO is used to obtain the optimal SVR parameters for predicting exchange rates. Our analysis involves the monthly exchange rates from January 1971 to December 2017 of seven countries including Australia, Canada, China, the European Union, Japan, Taiwan, and the United Kingdom. The out-of-sample forecast performance of the FSPSOSVR algorithm is compared with six competing forecasting models using the mean absolute percentage error (MAPE) and root mean square error (RMSE), including random walk, exponential smoothing, autoregressive integrated moving average (ARIMA), seasonal ARIMA, SVR, and PSOSVR. Our empirical results show that the FSPSOSVR algorithm consistently yields excellent predictive accuracy, which compares favorably with competing models for all currencies. These findings suggest that the proposed algorithm is a promising method for the empirical forecasting of exchange rates. Finally, we show the empirical relevance of exchange rate forecasts arising from FSPSOSVR by use of foreign exchange carry trades and find that the proposed trading strategies can deliver positive excess returns of more than 3% per annum for most currencies, except for AUD and NTD.

A Hybrid Intelligent Method for Compensation of Current Transformers Saturation Based on PSO-SVR

The Current Transformers (CT) saturation may cause the protective relays mal-operation either non-recognition of internal fault or undesirable trip under external fault conditions. Therefore, compensation of CT saturation is very important for correct performance of protective schemes. Compensation of CT saturation by combination of signal processing methods and intelligent algorithms is a suitable solution to solve the problem. It decreases the probability of mal-operation and increases the reliability of the power system. In this paper, Support Vector Regression (SVR) method is employed to compensate the distorted secondary current due to CT saturation. In SVR method, despite the other methods such as MLPand ANFIS, instead of minimizing the model error, the operational risk error is considered as target function. In this method, by using Kernel tricks, a smart RBF neural network is obtained, so that all operational procedures will be optimized automatically. In this paper, an intelligent method based on Particle Swarm Optimization (PSO) algorithm is presented to determine the optimal values of SVR parameters. Due to the stability and robustness of this method in presence of noise and sudden changes in current, this method has a high accuracy. In addition, a sample power system is simulated using PSCAD software. Afterwards, current signals are extracted and fed to PSO-SVR algorithm, which is implemented in MATLAB environment. The obtained results show the preference of the proposed method in aspect of estimation accuracy as compared to some presented methods in the field of CT saturation detection and correction.