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SVM is powerful for the problem with small samples, non linear and high dimension. But such important parameters as the kernel function parameters, the insensitive parameters and the penalty coefficient are determined based on experience and cross-validation in the SVM, so it has certain blindness. In the paper, support vector machine optimized particle algorithm is used to predict the intensity of...
A novel model was proposed for short-term electricity price forecasting based on rough set approach and improved support vector machines (SVM). Firstly, we can get reduced information table with no information loss by rough set approach. And then, this reduced information is used to develop classification rules and train SVM, at the same time, we make use of the particle swarm optimization to optimize...
This paper presents a new SVM algorithm framework optimized by PSO algorithm. The value of parameters in the SVM has great influence on the performance of regression model. In previous works the choice of these parameters mainly depends on the experience. In our work PSO algorithm was used to optimize these parameters to form a new SVM framework - PSVM. The proposed algorithm was used to forecast...
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