Study on Network Flow Prediction Model Based on Particle Swarm Optimization Algorithm and RBF Neural Network

被引：0

作者：

Bin, Zhang Yu ^{[1
]}

Zhong, Lin Li ^{[2
]}

Ming, Zhang Ya ^{[3
]}

机构：

[1] HeBei Vocat Art Coll, Shijiazhuang, Peoples R China

[2] ShiJiaZhuang Coll, Shijiazhuang, Peoples R China

[3] ShiJiaZhuang Informat Engn Vocat Coll, Shijiazhuang, Peoples R China

来源：

ICCSIT 2010 - 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, VOL 2 | 2010年

关键词：

network flow; neural network; particle swarm; optimization algorithm; prediction model;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

It is significant to control network congestion by time series forecasting research for network flow. The hybrid method of particle swarm optimization algorithm and RBF neural network is applied to predict network flow and gain the desirable network flow prediction results. In the hybrid method, particle swarm optimization algorithm is selected and adjusted to the connection weights and the center of radial basis function and the width of radial basis function. The network flow data are collected to search the prediction ability of particle swarm optimization algorithm and RBF neural network. Compared with the results of RBF neural network and BP neural network, particle swarm optimization algorithm and RBF neural network has better forecasting performance.

引用

页码：302 / 306

页数：5

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