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- W2385278403 abstract "Traditional RBF neural network in the forecasting process of the network traffic convergence is slow,prone to local optima and other shortcomings,resulting in low prediction accuracy and difficult problems.In order to improve the prediction accuracy of network traffic,network traffic prediction method for an ant colony algorithm to optimize the parameters of RBF neural network.Ant colony algorithm for training RBF neural network center and the width of the basis functions to speed up the convergence,simplifying the network structure to improve the generalization capability of RBF neural networks,and optimized RBF neural network to predict the network traffic,to prevent local optimum to appear.The experimental results show that relative to the commonly used BP neural network traffic prediction model,model predictions more accurate,can be well described by the variation of the network flow.With good generalization ability,good stability and a certain practical value in the prediction of network traffic." @default.
- W2385278403 created "2016-06-24" @default.
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- W2385278403 date "2012-01-01" @default.
- W2385278403 modified "2023-09-26" @default.
- W2385278403 title "Network Traffic Prediction Based on Neural Network Optimized by ACO" @default.
- W2385278403 hasPublicationYear "2012" @default.
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