Multi-kernel Support Vector Regression Research Articles

A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction

Battery life prediction is of great significance to the safe operation, and reduces the maintenance costs. This paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the battery. By feature extraction, eight features are obtained to fed into the life prediction model. The hybrid framework combines variational mode decomposition, the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward, uneven distribution of high-dimensional feature space and the local escape ability, respectively. Better parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm. The algorithm can improve the local escape ability and convergence performance and find the global optimum. The comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction performance. Compared with other algorithms, the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%. With the advance of the start point, the RUL prediction accuracy of the proposed algorithm does not change much.

Multi-kernel support vector regression optimization model and indirect health factor extraction strategy for the accurate lithium-ion battery remaining useful life prediction

Multi-kernel support vector regression optimization model and indirect health factor extraction strategy for the accurate lithium-ion battery remaining useful life prediction

Capacity degradation prediction of lithium-ion battery based on artificial bee colony and multi-kernel support vector regression

Lithium-ion battery (LIB) capacity degradation prediction plays an important role in the prediction of battery health degradation. Accurate prediction of its capacity can guide battery replacement and maintenance, and ensure the safety and stability of the energy storage system. In this paper, a hybrid method based on artificial bee colony (ABC) algorithm and multi-kernel support vector regression (MK-SVR) is proposed to predict the capacity degradation of LIB. Firstly, the capacity degradation prediction model of LIB is established by MK-SVR with few hyperparameters. Then, the hyperparameters of degradation prediction model are optimized by the ABC to improve the accuracy of prediction. Finally, the proposed method is verified by NASA's LIB degradation experiment. Compared with different optimization algorithms, ABC greatly improves the accuracy of capacity degradation prediction. Compared with traditional forecasting methods, the proposed method can predict the capacity degradation of LIB more accurately.

China's carbon dioxide emission forecast based on improved marine predator algorithm and multi-kernel support vector regression.

Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China’s carbon dioxide emissions during the “14th Five-Year Plan” period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China’s carbon dioxide emissions. During the “14th Five-Year Plan” period, China’s carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly.

Lithium Battery Remaining Useful Life Prediction Based on Multi-Kernel Support Vector Regression With Hybrid Optimization Algorithm

Abstract Accurately and reliably predicting the remaining useful life (RUL) of lithium battery is very important for the lithium battery health management system. However, most of the existing methods rely on complex multidimensional input features, which require a large number of sensors, increase the application cost and introduce redundant measurement errors. Therefore, this paper, only based on the battery capacity curve itself, proposes a method to construct a prediction model of support vector regression (SVR) by fusing multiple kernel functions. The linear equation coefficients of multiple kernel function combinations are optimized by the hybrid optimization algorithm. It is found that the hybrid kernel function can effectively overcome the problem that the single-kernel function is not capable of mapping the capacity fading trend of lithium battery. Hybrid optimization algorithm can avoid the problems of local optimization and global search ability deficiency. The proposed method is validated by experiments using the battery attenuation datasets from NASA, the University of Maryland, and a high-rate lithium battery in the laboratory stage. It can be seen from the experimental results that the prediction accuracy of this method is high. The mean prediction error, mean RMSE, and mean MAE are 2%, 0.0198, and 0.0157.

Multi Kernel Learning based Sugar Industry Load Forecasting

Sugar industry which plans for power usage from Bagasse also needs the load forecasting carried out using the energy audit data. The stochastic nature of the load demand of the sugar industry needs to be forecasted in advance for the assuring uninterrupted power delivery to the industry. The manual energy audit data obtained from the sugar industry for a period of time is obtained and trained on a regression based on Multi Kernel Learning (MKL). The Support Vector Regression (SVR) formulation is applied with the Multi Kernel topology and the performance parameters including the Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) is observed in the implementation. The algorithm is the Multi Kernel Support Vector Regression algorithm using the Python based toolbox.

Neurocomputing fundamental climate analysis

Rainfall is a natural phenomenon that needs to be studied more deeply and interesting to be analyzed. It involves numbers of human activities such as aviation, agriculture, fisheries, and also disaster risk reduction. Moreover, the characteristics of rainfall data follows seasonality, fluctuation, not normally distributed and it makes traditional time series challenging to use. Therefore, neurocomputing model can be used as an alternative to extraction information from rainfall data and give high performance also accuracy. In this paper, we give short preview about SST Anomalies in Manado, Northern Sulawesi and at the same time comparing the performance of rainfall forecasting by using three types of neurocomputing methods such as Generalized Regression Neural Network (GRNN), Feed forward Neural Network (FFNN), and Localized Multi Kernel Support Vector Regression (LMKSVR). In a nutshell, all of neurocomputing methods give highly accurate forecasting as well as reach low MAPE FFNN 1.65%, GRNN 2.65% and LMKSVR 0.28%, respectively.

Super-resolution of retinal images using multi-kernel SVR for IoT healthcare applications

Internet of Things (IoT) healthcare is one of the most popular areas of research due to the rapid development in information and communication technologies. IoT system focusing on human vision would be an ideal solution for the people in developing countries to have adequate medical attention. This paper proposes a hybrid architecture for IoT healthcare to process the retinal images captured using smartphone fundoscopy. The proposed super-resolution (SR) algorithm for retinal images use multi-kernel support vector regression (SVR) to improve the quality of the captured images. The experimental results with respect to the peak-signal-to-noise ratio (PSNR) and mean squared error (MSE) show that the proposed super-resolution approach for retinal images performs better when compared to the state-of-art algorithms. Further, the hybrid architecture helps the ophthalmologists in efficient diagnosis by providing high resolution retinal images.

基于多核支持向量回归的光谱反射率重建方法

基于多核支持向量回归的光谱反射率重建方法

MRI-based intelligence quotient (IQ) estimation with sparse learning.

In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject’s IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a single-kernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge.

Hybrid modeling of microwave devices using multi-kernel support vector regression with prior knowledge

This article proposes a support-vector hybrid modeling method of microwave devices when only a small number of measurements are available. In this method, a hybrid model of microwave device has been obtained by combining a coarse model and a support-vector model, where the coarse model is complemented by a support-vector model capable of correcting the difference between the measurements and the coarse model. The support-vector model was developed using a novel algorithm. In the algorithm, multi-kernel and prior knowledge from a calibrated simulator were incorporated into the framework of the linear programming support vector regression by utilizing multiple feature spaces and modifying the optimization formulation. The experimental results from two microwave devices show that the hybrid modeling can enhance the physical meaning of the support-vector model and improve the modeling accuracy for a small dataset, and that the proposed algorithm shows great potential in some applications where sufficient experimental data is difficult and costly to obtain, but the prior knowledge from a simulation model is available. The hybrid modeling is suited to a microwave computer-aided design tool or an automatic tuning robot. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:219–228, 2015.

Sparse multikernel support vector regression machines trained by active learning

A method for the sparse multikernel support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity ratio and allows the user to adjust the complexity of the resulting models. The sparse representation is guaranteed by limiting the number of training data points for the support vector regression method. Each training data point is selected based on its influence on the accuracy of the model using the active learning principle. A different kernel function is attributed to each training data point, yielding multikernel regressor. The advantages of the proposed method are illustrated on several examples and the experiments show the advantages of the proposed method.

Virtual Machines Performance Modeling with Support Vector regressions

Virtualization is a key technology in cloud computing to render on-demand provisioning of virtual services. Xen, an open source paravirtualized virtual machine monitor (hypervisor), has been adopted by many leading data centers of the world today. A scheduler in Xen handles CPU resources sharing among virtual machines hosted on the same physical system. This study is focused on a scheduler in the current Xen release - the Credit scheduler. Credit uses two parameters (weight and cap) to fine tune CPU resources sharing. Previous studies have shown that these two parameters can impact various performance measures of virtual machines hosted on Xen. In this study, we present a holistic procedure to establish performance models of virtual machines. Empirical data of two commonly used measures, namely calculation power and network throughput, were collected by simulations under various settings of weight and cap. We then employed a powerful machine learning tool (multi-kernel support vector regression) to learn performance models from the empirical data. These models were evaluated satisfactorily by using established procedures in machine learning.

Regression Using Multikernel and Semiparametric Support Vector Algorithms

In this letter, we propose a sequential training scheme for multikernel support vector regression (SVR). Unlike the multistage backfitting technique; our method re-tunes, at every stage, all previously trained weights using a semiparametric algorithm in the presence of one more kernel function. In this way, local minima are avoided and any combination of arbitrary kernel functions is acceptable. By experimenting on some synthetic and real data sets, we demonstrate that our method yields a better trade-off between sparsity and accuracy in comparison with the conventional single-kernel SVR and the multikernel backfitting SVR.