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Kernel function implicitly maps data from its original space to a higher dimensional feature space. Kernel based machine learning algorithms are typically applied to data that is not linearly separable in its original space. Although kernel methods are among the most elegant part of machine learning, it is challenging for users to define or select a proper kernel function with optimized parameter...
Smart grid infrastructure is an integration of advanced communication, sensing and computing techniques into the existing physical electrical grid. It is emerging as a critical cyber-physical system (CPS) infrastructure. CPS comes across as systems with a tight integration between the physical and the cyber layers in addition to the communication network. This exposes it to a risk of mis-operation...
This paper presents a hybrid approach for online fault detection in nonlinear processes. To solve the possible monitoring difficulties caused by nonlinear characteristics of industrial process data, two applications of the Kernel Method: Hypersphere Support Vector Machine (HSSVM) and Kernel Principal Component Analysis (KPCA) are used as fault detection methods. On top of that, to obtain the adaptive...
According to the freeway traffic flow characteristics under incidents situation, this paper puts forward a kind of AID algorithm based on PCA & SVM. Above all, PCA has been applied to extract principle components from preselected indexes, to realize dimension reductions, and construct the traffic eigenvectors. And then, validity of the new algorithm proposed in this paper has been tested using...
The performance of a kernel-based method is usually sensitive to a choice of the values of the hyper parameters of a kernel function. In this paper, we present a novel framework of using wavelet kernels in the kernel principal component analysis (KPCA) in order to better explain the nonlinear relationships among original multivariate data. We propose to introduce dilation and translation factors into...
In this research, optimized SVM models were designed to describe eutrophication processes, based on the field measured data from Bohai Bay. A new data-driven model called Support Vector Machine (SVM) based on structural risk minimization principle was presented, which minimized a bound on a generalized risk. In the eutrophication model, the Principal Component Analysis (PCA) was used to identify the...
A least squares support vector regression(LS-SVR) model for cement clinker calcination has been proposed, and successfully applied to an annual clinker production capacity of 0.73 million ton of Jiuganghongda Cement Plant in China. For the influence of unavoidable outliers in training sample on free calcium oxide (f-CaO) content and the degree of correlation between the original variables, a novel...
This paper investigates the use of a one-class support vector machine algorithm to detect the onset of system anomalies, and trend output classification probabilities, as a way to monitor the health of a system. In the absence of “unhealthy” (negative class) information, a marginal kernel density estimate of the “healthy” (positive class) distribution is used to construct an estimate of the negative...
The performance of Combined Support Vector Machines, C-SVM, is examined by comparing it's classification results with k-nearest neighbor and simple SVM classifier. For our experiments we use training and testing data obtained from two benchmark industrial processes. The first set is simulated data generated from Tennessee Eastman process simulator and the second set is the data obtained by running...
Load modeling plays an important role in power system stability analysis and planning studies. The parameters of load models may experience variations in different application situations. Choosing appropriate parameters is critical for dynamic simulation and stability studies in power system. This paper presents a method to select the parameters with good generalization ability based on a given large...
Since the early demonstration of the curative potential of radiation therapy for tumor sterilization, normal tissue toxicity continues to be dose limiting. Accurate prediction of patient??s complication risk would allow personalization of treatment planning decisions. Nonlinear kernel methods can provide a robust framework for learning complex interactions between observed toxicities and treatment,...
In the analysis of predicting financial distress based on support vector machine (SVM), irrelevant or correlated features in the samples could spoil the performance of the SVM classifier, leading to decrease of prediction accuracy. On the other hand, the improper determining of two SVM parameters will cause either over-fitting or under-fitting of a SVM model. In order to solve the problems mentioned...
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