Predicting nonlinear network traffic using fuzzy neural network
Network traffic is a complex and nonlinear process, which is significantly affected by immeasurable parameters and variables. This paper addresses the use of the five-layer fuzzy neural network (FNN) for predicting the nonlinear network traffic. The structure of this system is introduced in detail....
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| Main Authors: | , , , |
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| 格式: | text |
| 語言: | English |
| 出版: |
Institutional Knowledge at Singapore Management University
2003
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| 主題: | |
| 在線閱讀: | https://ink.library.smu.edu.sg/sis_research/5489 https://ink.library.smu.edu.sg/context/sis_research/article/6492/viewcontent/Predicting_nonlinear_network_traffic_fuzzyNeuralNetwork_ICICS03_av.pdf |
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| 總結: | Network traffic is a complex and nonlinear process, which is significantly affected by immeasurable parameters and variables. This paper addresses the use of the five-layer fuzzy neural network (FNN) for predicting the nonlinear network traffic. The structure of this system is introduced in detail. Through training the FNN using back-propagation algorithm with inertia] terms the traffic series can be well predicted by this FNN system. We analyze the performance of the FNN in terms of prediction ability as compared with solely neural network. The simulation demonstrates that the proposed FNN is superior to the solely neural network systems. In addition, FNN with different fuzzy reasoning approaches is discussed. |
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