Optimization-based Support Vector Regression Research Articles

Modeling magnetic refrigeration capacity of doped EuTiO3 magnetocaloric compounds using swarm based intelligent computational method

This work models the magnetic refrigeration capacity (MRC) of doped europium titanate (EuTiO3) at different applied magnetic field using hybrid particle swarm optimization based support vector regression (SW-SVR) algorithm with ionic radii dopant descriptors. The developed model with Gaussian function (SW-SVR-Gas) shows better performance as compared with polynomial based model (SW-SVR-Pol) with improvement of 35.76 %, 0.13 % and 45.76 % using mean absolute error (MAE), correlation coefficient (CC) and root mean square error (RMSE) performance yardsticks, respectively. The developed SW-SVR-Gas model investigates the behavior of Eu1-xBaxTiO3 and EuTi1-xNixO3 doped europium titanate at different dopants concentration as well as applied field. Novelties of the developed model include circumvention of experimental difficulties associated with magnetic refrigeration capacity determination, implementation of simple molecular descriptors with high precision and accuracy. The demonstrated uniqueness of the developed model would strengthen exploration of refrigeration capacity of europium titanate based compounds for addressing global refrigeration crisis.

Modeling magnetocaloric effect of doped EuTiO3 perovskite for cooling technology using swarm intelligent based support vector regression computational method

This work develops particle swarm optimization based support vector regression (PSVR) algorithm for predicting maximum magnetic entropy change using ionic radii of the constituting elements as well as the applied magnetic field as descriptors. The developed PSVR-G using Gaussian kernel function performs better than PSVR-P which employs polynomial data-mapping function with performance improvement of 0.04%, 9.93% and 5.25% on the basis of correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE), respectively on testing data samples. The influence of magnetic field ranging between 1 T and 5 T on doped perovskite EuTiO3 was investigated for different samples using the developed PSVR-G model. The significance of various dopants on perovskite EuTiO3 was further examined using the developed PSVR-G model. The accuracy and precision associated with the developed models would facilitate comprehensive exploration of perovskite EuTiO3 material for magnetic cooling application without experimental stress.

Power-Weighted Prediction of Photovoltaic Power Generation in the Context of Structural Equation Modeling

With the popularization of solar energy development and utilization, photovoltaic power generation is widely used in countries around the world and is increasingly becoming an important part of new energy generation. However, it cannot be ignored that changes in solar radiation and meteorological conditions can cause volatility and intermittency in power generation, which, in turn, affects the stability and security of the power grid. Therefore, many studies aim to solve this problem by constructing accurate power prediction models for PV plants. However, most studies focus on adjusting the photovoltaic power station prediction model structure and parameters to achieve a high prediction accuracy. Few studies have examined how the various parameters affect the output of photovoltaic power plants, as well as how significantly and effectively these elements influence the forecast accuracy. In this study, we evaluate the correlations between solar irradiance intensity (GHI), atmospheric density (ρ), cloudiness (CC), wind speed (WS), relative humidity (RH), and ambient temperature (T) and a photovoltaic power station using a Pearson correlation analysis and remove the factors that have little correlation. The direct and indirect effects of the five factors other than wind speed (CC) on the photovoltaic power station are then estimated based on structural equation modeling; the indirect effects are generated by the interaction between the variables and ultimately have an impact on the power of the photovoltaic power station. Particle swarm optimization-based support vector regression (PSO-SVR) and variable weights utilizing the Mahalanobis distance were used to estimate the power of the photovoltaic power station over a short period of time, based on the contribution of the various solar radiation and climatic elements. Experiments were conducted on the basis of the measured data from a distributed photovoltaic power station in Changzhou, Jiangsu province, China. The results demonstrate that the short-term power of a photovoltaic power station is significantly influenced by the global horizontal irradiance (GHI), ambient temperature (T), and atmospheric density (ρ). Furthermore, the results also demonstrate how calculating the relative importance of the various contributing factors can help to improve the accuracy when estimating how powerful a photovoltaic power station will be. The multiple weighted regression model described in this study is demonstrated to be superior to the standard multiple regression model (PSO-SVR). The multiple weighted regression model resulted in a 7.2% increase in R2, a 10.7% decrease in the sum of squared error (SSE), a 2.2% decrease in the root mean square error (RMSE), and a 2.06% decrease in the continuous ranked probability score (CRPS).

Hydrological peaks evaluation at Chitral and Tarbela stations, Pakistan, using combined Bayesian regularized neural network and signal difference average based variational mode decomposition method: A case study

Abstract Pakistan, being an agricultural country, highly depends on its natural water resources which originate from the upper regions of Hindu Kush-Karakoram-Himalaya Mountains and nourish one of the world's largest Indus Basin irrigation systems. This paper presents streamflow modelling and forecasting using signal difference average (SDA) based variational mode decomposition (VMD) combined with machine learning (ML) methods at Chitral and Tarbela stations on the Indus River network. For this purpose, VMD based random forest (VMD-RF), gradient boosting machine (VMD-GBM) and Bayesian regularized neural network (VMD-BRNN) have been chosen. Moreover, traditional time series flow models, that is seasonal autoregressive integrated moving average (SARIMA) and classical decomposition approach with particle swarm optimization-based support vector regression (PSO-SVR), are considered as benchmark models for comparison. The results show that overall, VMD-BRNN performed best, followed by VMD-GBM and VMD-RF, whereas SARIMA and PSO-SVR ranked last. Overall, SARIMA and PSO-SVR are failing to capture most of the peaks even during the training period whereas hybridization of VMD and ML methods has shown increased robustness of the models. The results show that the influential role of the high dimensional components and robustness on the river flow may be explored by most optimum SDA based VMD signals hybrid with BRNN method.

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm.

We developed particle swarm optimization-based support vector regression (PSVR) and ordinary linear regression (OLR) models for estimating the refractive index (n) and energy gap (E) of a polyvinyl alcohol composite. The n-PSVR model, which can estimate the refractive index of a polyvinyl alcohol composite using the energy gap as a descriptor, performed better than the n-OLR model in terms of root mean square error (RMSE) and mean absolute error (MAE) metrics. The E-PSVR model, which can predict the energy gap of a polyvinyl alcohol composite using its refractive index descriptor, outperformed the E-OLR model, which uses similar descriptor based on several performance measuring metrics. The n-PSVR and E-PSVR models were used to investigate the influences of sodium-based dysprosium oxide and benzoxazinone derivatives on the energy gaps of a polyvinyl alcohol polymer composite. The results agreed well with the measured values. The models had low mean absolute percentage errors after validation with external data. The precision demonstrated by these predictive models will enhance the tailoring of the optical properties of polyvinyl alcohol composites for the desired applications. Costs and experimental difficulties will be reduced.

Condition Monitoring of DC-Link Electrolytic Capacitors in PWM Power Converters Using OBL Method

Since the lifespan of an electrolytic capacitor is relatively short compared to other power semiconductor devices, the failure rate accounts for 60% and, thus, it is the most vulnerable component of the power conversion device. Therefore, the accurate measurement of the lifetime of an electrolytic capacitor is very important in ensuring the reliability of the entire system, including the capacitor. In this paper, an online failure detection method for a DC-link electrolytic capacitor in a back-to-back Pulse width Modulation (PWM) converter using the opposition-based learning particle swarm optimization-based Support Vector Regression (OPSO-SVR) technique is proposed. In this method, the capacitance and the DC-link capacitor power have been used in offline mode for SVR training and testing. During the offline mode, the SVR parameters have been optimized with the OPSO algorithm to use online to estimate the real value of the DC-link capacitor. The experimental results prove the superiority of the proposed technique over the SVR.

Development of a particle swarm optimization based support vector regression model for titanium dioxide band gap characterization

Energy band gap of titanium dioxide (TiO2) semiconductor plays significant roles in many practical applications of the semiconductor and determines its appropriateness in technological and industrial applications such as UV absorption, pigment, photo-catalysis, pollution control systems and solar cells among others. Substitution of impurities into crystal lattice structure is the most commonly used method of tuning the band gap of TiO2 for specific application and eventually leads to lattice distortion. This work utilizes the distortion in the lattice structure to estimate the band gap of doped TiO2, for the first time, through hybridization of a particle swarm optimization algorithm (PSO) with a support vector regression (SVR) algorithm for developing a PSO-SVR model. The precision and accuracy of the developed PSO-SVR model was further justified by applying the model for estimating the effect of cobalt-sulfur co-doping, nickel-iodine co-doping, tungsten and indium doping on the band gap of TiO2 and excellent agreement with the experimentally reported values was achieved. Practical implementation of the proposed PSO-SVR model would further widen the applications of the semiconductor and reduce the experimental stress involved in band gap determination of TiO2.

Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting.

The tourism industry has become one of the most important economic sectors for governments worldwide. Accurately forecasting tourism demand is crucial because it provides useful information to related industries and governments, enabling stakeholders to adjust plans and policies. To develop a forecasting tool for the tourism industry, this study proposes a method that combines feature selection (FS) and support vector regression (SVR) with particle swarm optimization (PSO), named FS–PSOSVR. To ensure high forecast accuracy, FS and a PSO algorithm are employed to, respectively, select reliable input variables and to identify the optimal initial parameters of SVR. The proposed method was tested using a data set of monthly tourist arrivals to Taiwan from January 2006 to December 2016. The results reveal that the errors obtained using FS–PSOSVR are comparatively smaller than those obtained using other methods, indicating that FS–PSOSVR is an effective method for forecasting tourism demand.

Remaining capacity estimation of lithium-ion batteries based on the constant voltage charging profile.

Estimation of remaining capacity is essential for ensuring the safety and reliability of lithium-ion batteries. In actual operation, batteries are seldom fully discharged. For a constant current-constant voltage charging mode, the incomplete discharging process affects not only the initial state but also processed variables of the subsequent charging profile, thereby mainly limiting the applications of many feature-based capacity estimation methods which rely on a whole cycling process. Since the charging information of the constant voltage profile can be completely saved whether the battery is fully discharged or not, a geometrical feature of the constant voltage charging profile is extracted to be a new aging feature of lithium-ion batteries under the incomplete discharging situation in this work. By introducing the quantum computing theory into the classical machine learning technique, an integrated quantum particle swarm optimization–based support vector regression estimation framework, as well as its application to characterize the relationship between extracted feature and battery remaining capacity, are presented and illustrated in detail. With the lithium-ion battery data provided by NASA, experiment and comparison results demonstrate the effectiveness, accuracy, and superiority of the proposed battery capacity estimation framework for the not entirely discharged condition.

Simulating the Discharge Coefficient of an Improved Triangular Side Weir using a Novel Hybrid Method of Firefly Optimization based Support Vector Regression

Simulating the Discharge Coefficient of an Improved Triangular Side Weir using a Novel Hybrid Method of Firefly Optimization based Support Vector Regression

Predicting Taxi-Out Time at Congested Airports with Optimization-Based Support Vector Regression Methods

Accurate prediction of taxi-out time is significant precondition for improving the operationality of the departure process at an airport, as well as reducing the long taxi-out time, congestion, and excessive emission of greenhouse gases. Unfortunately, several of the traditional methods of predicting taxi-out time perform unsatisfactorily at congested airports. This paper describes and tests three of those conventional methods which include Generalized Linear Model, Softmax Regression Model, and Artificial Neural Network method and two improved Support Vector Regression (SVR) approaches based on swarm intelligence algorithm optimization, which include Particle Swarm Optimization (PSO) and Firefly Algorithm. In order to improve the global searching ability of Firefly Algorithm, adaptive step factor and Lévy flight are implemented simultaneously when updating the location function. Six factors are analysed, of which delay is identified as one significant factor in congested airports. Through a series of specific dynamic analyses, a case study of Beijing International Airport (PEK) is tested with historical data. The performance measures show that the proposed two SVR approaches, especially the Improved Firefly Algorithm (IFA) optimization-based SVR method, not only perform as the best modelling measures and accuracy rate compared with the representative forecast models, but also can achieve a better predictive performance when dealing with abnormal taxi-out time states.

Firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir's discharge coefficient

A principal step in designing dividing hydraulic structures entails determining the side weir discharge coefficient. In this study, Firefly optimization-based Support Vector Regression (SVR-FF) is introduced and examined in terms of predicting the discharge coefficient of a modified labyrinth side weir. Ten non-dimensional parameters of various geometrical and hydraulic conditions are defined as the input parameters for the SVR-FF and the side weir discharge coefficient is defined as the output. Improvements in SVR prediction accuracy are determined by comparing SVR-FF with the traditional SVR model. The results indicate that the SVR-FF model with RMSE of 0.035 is about 10% more accurate than SVR with RMSE of 0.039. Thus, combining the Firefly optimization algorithm with SVR increases the prediction model performance.

Accurately Predicting High Temperature Flow Stress of AZ80 Magnesium Alloy with Particle Swarm Optimization-based Support Vector Regression

Accurately Predicting High Temperature Flow Stress of AZ80 Magnesium Alloy with Particle Swarm Optimization-based Support Vector Regression

Particle Swarm Optimization Based Support Vector Regression for Blind Image Restoration

This paper presents a swarm intelligence based parameter optimization of the support vector machine (SVM) for blind image restoration. In this work, SVM is used to solve a regression problem. Support vector regression (SVR) has been utilized to obtain a true mapping of images from the observed noisy blurred images. The parameters of SVR are optimized through particle swarm optimization (PSO) technique. The restoration error function has been utilized as the fitness function for PSO. The suggested scheme tries to adapt the SVM parameters depending on the type of blur and noise strength and the experimental results validate its effectiveness. The results show that the parameter optimization of the SVR model gives better performance than conventional SVR model as well as other competent schemes for blind image restoration.