The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This electronic For the limitations of dependence on previous experience and neural network forecasting model in current thunderstorm prediction. Considering the characteristics of the thunderstorm in Chongqing, the thunderstorm prediction model based on least square support vector machine (LS-SVM) is established. The data are preprocessed by principal component analysis(PCA) firstly. Then, the search...
FTIR micro-spectroscopic imaging analysis is a new and potential tool for subtle changes detection in biochemical composition. The excellent performance and limitation of FTIR technique are briefly discussed in this paper. In order to explore an automatic and efficacious method for chemical composition distinction, principal component analysis (PCA) and least square support vector machine (LS-SVM)...
In dealing with the problem that the important parameters of a penicillin fermentation process are hard to measure precisely, such as biomass concentration and production concentration, therefore, a soft sensor modeling for the penicillin fermentation based on fuzzy c-means clustering and least square support vector machine (LS-SVM) is proposed. First of all, features of sample data are extracted...
Visible and near infrared (NIR) spectroscopy was utilized to classify the verities of laver. As there are almost six hundreds of NMR variables which would cause poor classification and long calculation time, uninformative variables should be eliminated. Successive projections algorithm (SPA) was applied to select the effective variables from the full-spectrum (FS). Finally 13 variables were selected,...
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...
In this paper, a novel approach combining kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) is proposed for HVAC fan machinery status monitoring and fault diagnosis, which combines KPCA for fault feature extraction and multiple SVMs (MSVMs) for identification of different fault sources. KPCA is used as a preprocessor of LSSVM, which maps the original input...
In this paper, an electronic nose data classification approach based on least square support vector machines (LS-SVM) in combination with principal component analysis (PCA) is investigated. The electronic nose data are first converted into PCA, where the data are projected from a high dimensional space into a low dimensional space, preferably two or three dimensions. Then the resulting features from...
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...
The water supply forecasting is of great use both for the decision-making and management of water plan, and for the security of social basic lives. But due to the restriction on the getable data and the complexity of water flow, the water supply forecasting has indeed formed a typical nonlinear regression problem, which is still unsolved by traditional methods. To solve above problem, a novel forecasting...
Load forecasting plays a key role in power system operation and planning. However, the influencing factors of electric power load are very complex and variable. To achieve higher precision, as many of factors as possible are input in the forecast model at the cost of complex computing. Principal components analysis (PCA) is one of multivariate statistic analysis, which achieves parsimony and reduces...
Kernel principal component analysis (PCA) is a technique to perform feature extraction in a high-dimensional feature space, which is nonlinearly related to the original input space. The kernel PCA formulation corresponds to an eigendecomposition of the kernel matrix: eigenvectors with large eigenvalues correspond to the principal components in the feature space. Starting from the least squares support...
This study investigated multi-spectral imaging technique as a rapid method to discriminate the tea category. Tea was spread over the whole images. The images for each sample were captured using a red, near infrared and green channel multi-spectral camera. 320 images were obtained. Three texture features were obtained through the entropy of three channels and then set as the input variables for pattern...
Week-ahead load forecasting is essential in the planning activities of every electricity production and distribution company. This paper proposes the application of principal component analysis (PCA) to least squares support vector machines (LS-SVM) in a week-ahead load forecasting problem. New realistic features are added to better and more efficiently train the model. For instance, it was found...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.