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- W2348358596 abstract "Based on the single kernel function relevance vector machine(RVM) models,it constructs multi-ple middle-time-load-forecasting models.The RVM’s kernel functions were linearly compounded by Gauss-ian kernel with polynomial kernel and tensor product spline kernel,and the compounding kernels’parame-ters are optimized by algorithm of particle swarm optimization(PSO).With the training and testing sample data of 2001 world-wide competition of electricity load forecasting,all the models’forecasting value were given.The results show,although the every model has a good accuracy,all the multi-linearity-compoun-ding kernels RVM models give the better accuracy than the single kernel ones.Besides the forecasting error bar also be given based on the exclusive probability character of relevance vector machine." @default.
- W2348358596 created "2016-06-24" @default.
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- W2348358596 date "2010-01-01" @default.
- W2348358596 modified "2023-09-24" @default.
- W2348358596 title "Relevance vector machine based on particle swarm optimization of compounding kernels in electricity load forecasting" @default.
- W2348358596 hasPublicationYear "2010" @default.
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