Paper Info
Title
Research and Prediction on Nonlinear Network Flow of Mobile Short Message Based on Neural Network
Abstract
At present, research on nonlinear network flows of mobile short message is one hotspot in mobile communications fields. Nonlinear network flows of mobile short message have such essential features varying with time as periodicity, regularity, correlation, randomicity, occasionality. The traditional methods based on linear models are successful relatively in making irregular flow series become more regular, but compared with actual results, the forecasting results have more obvious deviations. According to real data measured, this paper has established a forecasting model of mobile short message network flows based on time series and improved bp algorithm, and used this model to research issues forecasting nonlinear network flows of mobile short messages. The experiment has shown that the forecasting model has higher precision and better extension compared with traditional models.
Year
DOI
Venue
2010
10.1109/GrC.2010.80
GrC
Keywords
Field
DocType
forecasting result,neural network,mobile communication,linear model,forecasting theory,research issue,mobile short message,forecasting model,irregular flow series,telecommunication congestion control,backpropagation,network flow,bp algorithm,mobile short message service,electronic messaging,prediction theory,mobile communications field,traditional model,mobile short message network,nonlinear network flow,prediction model,time series,neural nets,artificial neural networks,forecasting,predictive models,mobile computing
Flow network,Mobile computing,Nonlinear system,Linear model,Computer science,Artificial intelligence,Artificial neural network,Backpropagation,Hotspot (Wi-Fi),Mobile telephony,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-7964-1
0
0.34
References 
Authors
2
3
Name
Order
Citations
PageRank
Nianhong Wan100.34
Ji-yi Wang2178.05
Xuerong Wang320.79