Information Technology Journal1812-56381812-5646Asian Network for Scientific Information10.3923/itj.2014.593.596LiGuodongJinPengfeiLiKai32014133The existence of time-varying network delay will have some
negative impacts on some network services. The dynamic changes of network delay
reflect the load characteristics of network path. An important basis for the
implementation of congestion control and routing is the accurate prediction
of network delay. In this paper, a delay prediction model based on Radial Basis
Function (RBF) neural network is establishe which is trained by APC-III algorithm
and the method of least squares. This model avoids the cumbersome process of
model structure identification and model checking in traditional time-series
analysis and overcomes the shortcoming that the traditional neural network can
easily fall into local extremum and overtraining. The simulation results show
that this model can predict network delay with high accuracy.]]>Paxson, V.,1999Allman, M. and V. Paxson,1999Johari, R. and D.K.H. Tan,2001Wong, J.W.,1978Jiao, L., D. Zhang and B. Houjie,2006Li, Q. and D.L. Millis,2001Yang, M. and X.R. Li,2003Parlos, A.G.,2002Srikar, D.,2004Zhang, F.,2003Hwang, Y.S. and S.Y. Bang,1994