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In order to predict blended coal's property accurately, a new kind of hybrid prediction model based on principal component analysis (PCA) and weighted support vector machine (WSVM) was established. PCA was used to transform the high-dimensional and correlative influencing factors data to low-dimensional principal component subspace. These new features are(More)
Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) is a powerful tool for modeling the inputs and output(s) of complex and nonlinear systems. However, parameters determination for a SVR model is competent to the forecasting accuracy. Several evolutionary algorithms, such as genetic(More)
In this paper, a novel building cooling load forecasting approach combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. KPCA is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method. The original inputs are firstly transformed into nonlinear(More)
Forecasting agriculture water demand is significant to optimize confirmation of water resources. In this study, we introduce a hybrid model which combines rough set theory and least square support vector machine to forecast the agriculture irrigation water demand. Through a certain district agriculture irrigation water demand dataset experiment, we have(More)
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