Network communication load state recognition model based on correlation vector machine

Abstract

Network communication load state recognition has important practical application value, in order to improve the network communication load state recognition accuracy is proposed based on relevance vector machine load of the network traffic state identification model. When the network throughput overload, need to adjust the network communication strategy to ensure the overall performance of sensor network, so first of all need to network transmission state identification. Firstly, uses wavelet analysis in order to eliminate the noise signal, and extract the features, and then the artificial bee colony optimization algorithm to determine the optimal parameters of relevance vector machine, establishing the network communication load state identification model, finally through with other network communication load condition recognition models to be compared with the experimental. The results show that the model can describe the relationship between the state and the characteristics of network communication, and improve the recognition accuracy of the network traffic load state, and the recognition results are higher than the comparison model.