Anzeige
Erschienen in:
2016 | OriginalPaper | Buchkapitel
Bandwidth Forecasting for Power Communication Using Adaptive Extreme Learning Machine
verfasst von : Zheng Zheng, Li Di, Song Wang, Min Xia, Kai Hu, Ruidong Zhang
Erschienen in: Cloud Computing and Security
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Bandwidth demand forecasting is the basis and foundation of the power communication network planning. In view of the traditional neural network learning speed is slow, the number of iterations is large, and the local optimal problem, an adaptive extreme learning machine model based on the theory of extreme learning machine and K nearest neighbor theory is proposed to predict the bandwidth of electric power communication. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and reduce the effect of the overfitting of networks. The proposed algorithms are validated using real data of a province in China. The results show that this method is better than the traditional neural network, auto regressive model, self organization model, and single extreme learning machine model. It can be used in electric power communication bandwidth prediction.