When the support vector machine is used for load forecasting, the input samples of support vector machine have important effect to forecasting results. Support vector machine can study any non-linear relation, but if a group of non-distinct variables are selected as input variable set, the training time of support vector machine is lengthened and the errors become bigger. The non-linear relation of the load can be effectively explained only when a group of appropriate input variables are found. In this paper, the correlation coefficient idea is used to input variables selection of support vector machine short-term load forecasting model. The load values, which have bigger correlation coefficient with expectation output values, are chosen from effect factor sets as input variables. By mean of this method, a preferable input variables set can be gained, the correlation between the input variables and the forecasting points are bigger, and the forecasting results is more exact. The simulation results show that the method is effective.
In video surveillance, there are many interference factors such as target changes, complex scenes, and target deformation in the moving object tracking. In order to resolve this issue, based on the comparative analysis of several common moving object detection methods, a moving object detection and recognition algorithm combined frame difference with background subtraction is presented in this paper. In the algorithm, we first calculate the average of the values of the gray of the continuous multi-frame image in the dynamic image, and then get background image obtained by the statistical average of the continuous image sequence, that is, the continuous interception of the N-frame images are summed, and find the average. In this case, weight of object information has been increasing, and also restrains the static background. Eventually the motion detection image contains both the target contour and more target information of the target contour point from the background image, so as to achieve separating the moving target from the image. The simulation results show the effectiveness of the proposed algorithm.
In some developed countries, the automatic vehicle recognition is a quite mature technology. This paper applies the multi-classification method based on Support Vector Machine (SVM) to vehicle recognition. Support vector machine, appeared recently, is a new theory and technology in the filed of pattern recognition and has shown excellent performance in practice. This method was proposed basing on Structural Risk Minimization (SRM) in place of Experiential Risk Minimization (ERM), thus it has good generalization capability. By mapping input data into a high dimensional characteristic space in which an optimal separating hyperplane is built, SVM presents a lot of advantages for resolving the small samples, nonlinear and high dimensional pattern recognition, as well as other machine-learning problems such as function fitting. The simulation results show that the proposed method is effective and feasible.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.