The actual traffic prediction method based on particle swarm optimisation and wavelet neural network

被引：0
作者：
Chen K. [1 ]
Peng Z. [1 ]
Ke W. [1 ]
机构：
[1] Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, Guangdong
来源：
International Journal of Wireless and Mobile Computing | 2019年 / 17卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Congestion; Neural network; Particle swarm; Prediction;
D O I：
10.1504/IJWMC.2019.103109
中图分类号：
学科分类号：
摘要：
For the congestion phenomena of networks, it has been provided with a new prediction method for service flow (based on Particle Swarm Optimisation and Wavelet Neural Network Prediction PSOWNNP). Firstly, this method is using the wavelet exchange to resolve the service flow, and using its wavelet coefficient and metric coefficient as the sample data. Secondly, training the sample data is using the neural network method of the particle swarm optimisation in which it is applying the wavelet model for construction, and the prediction data for service flow will be obtained from this. At the same time, the prediction methods of wavelet neural network and BP neural network for particle swarm optimisation are analysed and compared through the simulation experiment, and the result for indicating the performance of AWNNP method is relatively good, with a tolerance of 17.21%. Copyright © 2019 Inderscience Enterprises Ltd.
引用
收藏
页码：317 / 322
页数：5
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