Multi-neural network-based traffic matrix estimation method

A flow matrix and neural network technology, applied in the field of flow matrix estimation based on multiple neural networks, can solve problems such as memory distortion, slow neural network training speed, memory disappearance, etc.

Active Publication Date: 2011-07-27
HUNAN UNIV
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Problems solved by technology

These will inevitably lead to a very slow training speed of the neural network, and the training of a large number of high-dimensional sample data will exacerbate the contradiction between the memory and plasticity of the traditional neural network, that is, the problem of memory distortion or memory disappearance

Method used

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  • Multi-neural network-based traffic matrix estimation method

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Embodiment Construction

[0075] This embodiment provides a method for estimating multiple neural networks based on a BP neural network. Using the OD traffic and link traffic of the Abilene IP backbone network in the United States in the last week of March and the first two weeks of April in 2004 as samples, the OD traffic in the next three weeks is estimated. It includes three steps of sample classification, sample training and traffic estimation.

[0076] 1. Sample classification

[0077] The K-means algorithm is used to classify the samples, and the number of classes K is determined using the Shi principle, where K=20. The Euclidean distance is used to represent the difference of the vectors during the classification process, and the K-means algorithm is used repeatedly until a stable classification is found. Calculate and record the center c of each classification according to (4)(5) i and radius d i , i=1, 2, . . . , K.

[0078] 2. Sample training:

[0079] Use BP neural network training for ...

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Abstract

The invention provides a multi-neural network-based traffic matrix estimation method, which can improve the accuracy of the conventional network traffic matrix estimation. According to the method, the problem of memory fading or deformation of the traditional neural network used in traffic matrix estimation can be effectively overcome by respectively training sampled traffics before classifying. The errors of the multi-neural network-based estimation is remarkably less than that of estimation based on the traditional neural network.

Description

technical field [0001] The invention relates to the field of network measurement and neural network, in particular to a flow matrix estimation method based on multiple neural networks. Background technique [0002] The traffic matrix is ​​an overview of the traffic of the whole network. The elements in the matrix represent the traffic (OD traffic) starting from one node (source node) and terminating at another node (destination node) in the network. This source and destination node pair is also called an OD pair. The measurement and calculation of OD traffic is of great significance in the research and engineering practice of network structure configuration, management, and network traffic engineering. The research on the measurement and calculation of OD flow has received extensive attention from the theoretical and industrial circles at home and abroad. Since the traffic matrix needs to capture the global state of network traffic, direct monitoring is very expensive and ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08H04L12/26
Inventor 张大方王晓阳
Owner HUNAN UNIV
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