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

Authors: Ke Chen; Zhiping Peng; Wende Ke

Addresses: Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, 525000, Guangdong, China ' Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, 525000, Guangdong, China ' Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, 525000, Guangdong, China

Abstract: 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%.

Keywords: congestion; prediction; particle swarm; neural network.

DOI: 10.1504/IJWMC.2019.103109

International Journal of Wireless and Mobile Computing, 2019 Vol.17 No.4, pp.317 - 322

Received: 24 Jul 2018
Accepted: 07 May 2019
Published online: 15 Oct 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article