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- W2359149603 abstract "Chaos is a kind of seemingly non-rule movement, referring to the seemingly random acts in a confirmed system without attaching any random factors.In chaotic research, it is important to predict the future of system according to the nonlinearity time series extracted from chaotic system. The nonlinearity of chaotic time series brings about difficulty to built prediction model . Relevance Vector Machine (RVM) is a new Statistical Learning method based on Supporting Vector Machine (SVM). Taking typical chaos time series as study object , the paper launches prediction research based on RVM. Experiment indicates that within several steps, the prediction using RVM is accurate, and it is suitable to apply RVM to chaotic time series multistep prediction." @default.
- W2359149603 created "2016-06-24" @default.
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- W2359149603 date "2008-01-01" @default.
- W2359149603 modified "2023-09-25" @default.
- W2359149603 title "Prediction of the Chaotic Time Series Based on RVM" @default.
- W2359149603 hasPublicationYear "2008" @default.
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