Research on fuzzy neural network algorithms for nonlinear network traffic predicting
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Abstract:
This paper addresses the use of fuzzy neural networks (FNN) for predicting the nonlinear network traffic. Through training the fuzzy neural networks with momentum back-propagation algorithm (MOBP) and choosing the appropriate activation function of output node, the traffic series can be well predicted by these structures. From the effective forecasting results obtained, it can be concluded that fuzzy neural networks can be well applicable for the traffic series prediction. In addition, the performance of the FNN was particularly discussed and analyzed in terms of prediction ability compared with solely neural networks. The effectiveness of the proposed FNN is demonstrated through the simulation. This work is supported in part by China Postdoctoral Foundation under grant (2005037529), Tianjin High Education Science Development Foundation under grant (20041325) and Education Ministry Doctoral Discipline Foundation of China under grant (2003005607).
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Clc Number:
TP393
