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In order to simulate human behavior to achieve intelligent control, in this paper a mathematical modeling method is presented based on Kernel Principal Component Analysis (KPCA) and Support Sector Machine. Sample data from the input space are mapped to high-dimensional feature space by non-linear transformation, then their features are extracted by PCA to decrease dimension of input vector and then...
This paper first provides a method for predicting fouling faults about flow passage of steam turbine based on kernel principal component analysis(KPCA) and least square support vector machine regression (LS-SVMR). First, KPCA is used to extract main features independent for each other from a lot of relaticve fault feature data. Afterwards, a model is established for predicting the trend of each main...
Dynamic State Estimation (DSE) for power system considers statistical characters of systemic state variables in past period, has functions of state estimation and forecasting. This paper proposes a new method for state estimation problem in power systems based on Kernel Principle Component Analysis (KPCA) and Support Vector Regression (SVR). Firstly, the KPCA can extract the nonlinear relationship...
We present an algorithm for detecting human actions based upon a single given video example of such actions. The proposed method is unsupervised, does not require learning, segmentation, or motion estimation. The novel features employed in our method are based on space-time locally adaptive regression kernels. Our method is based on the dense computation of so-called space-time local regression kernels...
A two-stage neural network architecture constructed by combining Support Vector Machines (SVM) with kernel principal component analysis (KPCA) and genetic algorithms (GAs) is proposed for technological achievements of college students forecasting. In the first stage, KPCA is used as feature extraction. In the second stage, KPCA SVM is used to regression estimation by finding the most appropriate kernel...
We present a novel approach to the problem of detection of visual similarity between a template image, and patches in a given image. The method is based on the computation of a local kernel from the template, which measures the likeness of a pixel to its surroundings. This kernel is then used as a descriptor from which features are extracted and compared against analogous features from the target...
Distribution centers site selection has become a popular problem in recent years. Fine distribution centers site selection can ensure the supply and reduce the cost. By studying the methods proposed by other scholars, a mew method, KPCA (kernel principal component analysis) -SVRM (support vector regression machine) is proposed by this paper. The first step of this method is to apply KPCA to SVRM for...
A new feature extraction method for high dimensional data using least squares support vector regression (LSSVR) is presented. Firstly, the expressions of optimal projection vectors are derived into the same form as that in the LSSVR algorithm by specially extending the feature of training samples. So the optimal projection vectors could be obtained by LSSVR. Then, using the kernel tricks, the data...
This paper is established in electricity lines, and put forward the viewpoint that we can build coal storage center to settle the problem of power generation coal supply. Make the coal storage centre site selection by using KPCA (kernel principal component analysis) -SVRM (support vector regression machine), taking all factors into account, and making the advantage of the social division of labor...
Least squares support vector machine (LSSVM) has been used in soft sensor modeling in recent years. In developing a successful model based on LSSVM, the first important step is feature extraction. Principal components analysis (PCA) is a usual method for linear feature extraction and kernel PCA (KPCA) is a nonlinear PCA developed by using the kernel method. KPCA can efficiently extract the nonlinear...
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