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Network flow prediction method and device based on cognitive network

A technology of network traffic and cognitive network, which is applied in the field of network traffic prediction based on cognitive network, can solve problems such as difficult to satisfy accurate description and prediction of network traffic

Inactive Publication Date: 2013-08-21
BEIJING JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

With the increase of network bandwidth and the emergence of various network services, the previous traffic prediction model has been difficult to accurately describe and predict the existing and future network traffic.

Method used

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  • Network flow prediction method and device based on cognitive network
  • Network flow prediction method and device based on cognitive network
  • Network flow prediction method and device based on cognitive network

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Experimental program
Comparison scheme
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Embodiment 1

[0031] refer to figure 1 , shows a flow chart of a cognitive network-based network traffic prediction method of the present invention, the method specifically includes:

[0032] Step S101, performing least squares processing on the input signal X(t), and outputting predicted sample data Y(t);

[0033]The least squares method can be used to process a set of data, and the dependence relationship between variables can be found from a set of measured data. This functional relationship is called an empirical formula. The cognitive network-based network traffic prediction method described in this embodiment can be understood as an anti-overfitting prediction model, which performs least square method (LMS) processing on input samples, and uses the processed prediction samples as input.

[0034] The following will introduce the precise definition of the least square method and how to find the empirical formula when the relationship between x and .y is approximately linear.

[0035] ...

Embodiment 2

[0087] refer to image 3 , which shows a structural diagram of a cognitive network-based network traffic prediction device according to the present invention, and the device specifically includes:

[0088] The first processing module 301 is configured to perform least squares processing on the input signal X(t), and output predicted sample data Y(t);

[0089] The second processing module 302 is used to perform wavelet transformation on Y(t), decompose it into components of different frequency components, and the wavelet transformation coefficient sequence {D 1 (k), D 2 (k),...D L (k), A L (k)};

[0090] The third processing module 303 is used to take the component {D 1 (k), D 2 (k),...D L (k)} as the input of the Elman network, the wavelet coefficient {D at time k+T 1 (k+T), D 2 (k+T),...D L (k+T)} as output to train the network;

[0091] The fourth processing module 304 is used to convert the component {A L (k)} as the input of the linear network, {A L (k+T)} to ...

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Abstract

The invention provides a network flow prediction method and device based on a cognitive network. The method comprises the following steps of: carrying out least square method processing on an input signal X(t); outputting prediction sample data Y(t); carrying out wavelet transformation on the Y(t); decomposing the Y(t) into components with different frequency compositions; carrying out wavelet transformation on a coefficient sequence {D1(k), D2(k), ...... DL(k), AL(k)} at the k moment; training the network with the component {D1(k), D2(k), ...... DL(k)} as input of an Elman network and a wavelet coefficient {D1(k+T), D2(k+T), ...... DL(k+T)} at the k+T moment as output; training the network with the component of {AL(k)} as input of a linear network and {AL(k+T)} as output; training the network with each trained wavelet component {D1(k+T), D2(k+T), ...... DL(k+T), AL(k+T)} as input of a BP network and the original flow time {f(k+T)} at the k+T moment as the network output; obtaining the prediction output; introducing an LMS (Least Mean Square) algorithm to pre-process the input sample aiming at advantages and disadvantages of the traditional flow model and prediction method; inputting the input sample to a WNN (Wavelet Neural Network) prediction model, therefore, the over-fitting problem in the traditional model is solved, and a more accurate model and prediction are provided for the network flow.

Description

technical field [0001] The present invention relates to the field of network information technology, in particular to a cognitive network-based network traffic prediction method and device. Background technique [0002] Cognitive technology is an intelligent wireless communication system that can perceive the surrounding environment, obtain information from the environment, and adapt to changes in the environment by changing parameters such as transmission power, carrier frequency, and modulation mode in real time. At present, the main achievements of cognitive technology are concentrated in the development and application of cognitive radio technology. Introducing cognitive technology into network system applications is called Cognitive Network, which is an important research field in the future communication field. The cognitive network needs to perceive the electromagnetic change characteristics of the surrounding environment, and make intelligent decisions based on the ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W16/22
Inventor 朱晓敏谷秀君张润彤尚小溥李丹丹华蕊李岩
Owner BEIJING JIAOTONG UNIV
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