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Efficient spectrum sensing can be realized by predicting the future idle times of primary users' activity in a cognitive radio network. In dynamic spectrum access, based on a reliable prediction scheme, a secondary user chooses a channel with the longest idle time for data transmission. In this paper, four supervised machine learning techniques, two from ANN, i.e. Multilayer Perceptron & Recurrent...
Making precise predictions about the future behavior of a system such as a country's economy, a firm or a lake, or about the population of some species of animal has always been a challenge. While prediction methods and modeling procedures have been developed and used over the past decades, the high degree of uncertainty and complexity that underlie some systems makes it difficult, and in some cases...
Combining phase space reconstruction theory and least squares support vector machines (LSSVM) method, a novel fault prediction model is proposed in this paper. The model reconstructs phase space for fault characteristics time series of the system and fit the nonlinear relationship of phase point evolution by use of least squares support vector machines according to the laws of phase space evolution...
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