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Wireless network flow rate prediction method based on LSTM network

A traffic forecasting and wireless network technology, applied in the direction of data exchange network, digital transmission system, electrical components, etc., can solve the problem of low accuracy of traffic forecasting, achieve the effect of avoiding poor long-term memory and improving accuracy

Active Publication Date: 2018-11-27
XIDIAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the defects in the above-mentioned prior art, and propose a wireless network traffic prediction method based on LSTM network, which is used to solve the technical problem of low traffic prediction accuracy existing in the prior art

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  • Wireless network flow rate prediction method based on LSTM network
  • Wireless network flow rate prediction method based on LSTM network
  • Wireless network flow rate prediction method based on LSTM network

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Embodiment Construction

[0032] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0033] refer to figure 1 , a wireless network traffic prediction method based on an LSTM network, comprising the steps of:

[0034] Step 1) Construct LSTM composite network:

[0035] The output of the LSTM network is used as the input of the linear regression network to obtain the LSTM composite network,

[0036] The LSTM network has long-term memory for data, fully considers the time correlation of data, and can automatically adjust the contribution of historical information to the current prediction according to the current state, and the linear regression network uses the output of the LSTM network to make predictions; figure 2It is a schematic structural diagram of the LSTM composite network adopted in the present invention;

[0037] Step 2) Obtain the training set data and test set data of the LSTM composite network:

[0038...

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Abstract

The invention provides a wireless network flow rate prediction method based on an LSTM network. The method is used for solving the technical problem of low prediction precision in the prior art. The method has the realization steps of constructing an LSTM composite network; obtaining a training set data and test set data of the LSTM composite network; initializing the parameter of an LSTM composite network; training the LSTM composite network; optimizing the trained LSTM composite network; predicting the flow rate on the future data. The long-period memory performance of the LSTM network on the flow rate data is sufficiently utilized; the contribution of the historical information on the current prediction can be automatically regulated according to the current state; the wireless networkflow rate prediction precision is improved; the method can be used in the fields of internet of vehicles, finance and the like.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and communication networks, and relates to a wireless network traffic prediction method, in particular to a wireless network traffic prediction method based on an LSTM network, which can be used in the fields of Internet of Vehicles and finance. Background technique [0002] The early network data transmission volume was small, and the application was relatively simple. Usually, the model of the public switched telephone network is used for reference, and the Poisson model is used to describe the traffic of the network, and good results have been achieved. With the development of network technology, the Poisson process can no longer fully reflect the characteristics of business traffic. Researchers have gradually introduced models such as Markov, autoregressive, and Kalman filtering to describe network traffic. Generally, these early models are traditional network traffic models. ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24
CPCH04L41/142H04L41/147
Inventor 刘伟曹淑琳
Owner XIDIAN UNIV
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