Network Traffic Prediction Using Least Mean Kurtosis(Fundamental Theories for Communications)

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概要

• 論文の詳細を見る

• Recent studies of high quality, high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. In this paper, Least Mean Kurtosis (LMK), which uses the negated kurtosis of the error signal as the cost function, is proposed to predict the self similar traffic. Simulation results show that the prediction performance is improved greatly over the Least Mean Square (LMS) algorithm.

• 社団法人電子情報通信学会の論文
• 2006-05-01

著者

• Zhao Hong

  Embry-riddle Aeronautical University

• Ansari Nirwan

  New Jersey Institute Of Technology

• SHI Yun

  New Jersey Institute of Technology

関連論文

• Network Traffic Prediction Using Least Mean Kurtosis(Fundamental Theories for Communications)

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