Abstract
Much of the earlier work presented in the area of on-line flow diagnosis focuses on knowledge based and qualitatively reasoning principles and attempts to present possible root causes and consequences in terms of various measured data. However, forecasting flow is an un-measurable operating variable in diagnosis processes that define the state of the network. Forecasting flow essentially characterize the efficiency and really need to be known in order to diagnose possible malfunction and provide a basis for deciding on appropriate action to be taken by network manager. This paper proposes a novel flow-predictable system (FPS) based on fuzzy neural network. The features of dynamic trends of the network flow are extracted using a decision matrix transform and a qualitative interpretation, and then are used as inputs in the neural network, so that it can be used to fit the smooth curves perfectly. It is adopts to deal with the mapping relation and categorizing the network faults. The experiment system implemented by this method shows the proposed system is an open and efficient flow-fault forecast engine.
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, Q., Xu, M., Zhang, H., Liu, F. (2005). Neural Network Based Flow Forecast and Diagnosis. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596981_80
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DOI: https://doi.org/10.1007/11596981_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30819-5
Online ISBN: 978-3-540-31598-8
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