This paper presents dynamic voltage collapse prediction on an actual power system using support vector regression. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domai...

This paper deals with the identification of nonlinear systems using multi-kernel approach. In this context, we have improved the Support Vector Regression (SVR) method in order to identify nonlinear complex system. Our idea consists in dividing the regressor vector in several blocks, and, for each one a kernel function is used. This blockwise SVR approach is called Support Kernel Regression (SK...

Predicting fatigue life of composite materials is essential to increase reliability of manufacturing systems. The predicting techniques for fatigue life of composite materials are not widely investigated. The support vector regression (SVR) is an emerging forecasting technique and has been applied in many areas successfully. Therefore, this study attempts to examine the feasibility of SVR in pr...

Agile software development process represents a major departure from traditional, plan-based approaches to software engineering. Estimating effort of agile software accurately in early stage of software development life cycle is a major challenge in the software industry. For improving the estimation accuracy, various optimization techniques are used. The Support Vector Regression (SVR) is one ...

Support Vector Regression (SVR), a category for Support Vector Machine (SVM) attempts to minimize the generalization error bound so as to achieve generalized performance. Regression is that of finding a function which approximates mapping from an input domain to the real numbers on the basis of a training sample. Support vector regression is the natural extension of large margin kernel methods ...

The ν-Support Vector Regression (ν-SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ν on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ν-Support Vector Classification (ν-SVC) (Schölkopf et al., 2000), ν-SVR introduces an additional linear term into its objective function. Thus, di...

In the color system of a computer, the nonlinearity of the image acquisition device and the display device may result in the difference between the colors displayed on the screen and the actual color of objects, which requires for color correction. This paper introduced the Support Vector Regression (SVR) to establish a color correction model for the nonlinear imaging system. In the modeling pr...

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computation...

In this paper we explore using the support vector regression (SVR) based on the statistics-learning theory of structural risk minimization for the regional logistics demand. Aiming at the blindness of man made choice of parameter and kernel function of SVR, we apply a chaos optimization method to select parameters of SVR. The proposed approach is used for forecasting logistics demand of Shangha...

In this paper, we introduce a new method: support vector regression (SVR) method to modeling low temperature co-fired ceramic (LTCC) multilayer interconnect. SVR bases on structural risk minimization (SRM) principle, which leads to good generalization ability. A LTCC based stripline-to-stripline interconnect used as example to verify the proposed method. Experiment results show that the develop...