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Estimation of forest stand parameters from airborne laser scanning data relies on the selection of laser metrics sets and numerous field plots for model calibration. In mountainous areas, forest is highly heterogeneous and field data collection labour-intensive hence the need for robust prediction methods. The aim of this paper is to compare stand parameters prediction accuracies of support vector...
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...
This paper proposes a new approach to solve the short term load forecasting problem that considers electricity price as one of the main characteristics of the system load. The proposed method is derived by integrating the kernel principal component analysis (KPCA) method with locally weighted support vector regression (LWSVR). LWSVR can be derived by modifying the risk function of the support vector...
With certainty plus a random time series analysis method, model TA is created with a combination of the trend analysis and ARMA. At the same time, using principal component analysis of input variables pre-set to eliminate the factors that affect the overlap between the information, support vector machine regression model, energy demand forecast to be the PS model. Then, model TA combining with model...
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...
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