Network traffic prediction based on Extreme Learning Machine and Least Square Support Vector Machine
Chen Hong-xin · Computer Engineering and Applications Journal · 2015
In order to improve the prediction accuracy, aiming at the defects of the over fitting in extreme learning machine,this paper proposes a novel network traffic prediction model based on Extreme Learning Machine and Least Square Support Vector Machine(ELM-LSSVM). The phase space reconstruction is used to build learning samples of network flow and then the training samples are input to ELM and are learnt in which the Least Squares Support Vector Machine are introduced into Extreme Learning Machine. The simulation experiment is carried out to test the performance. The results show that the proposed model has improved the prediction accuracy of network traffic and has strong practical application value.