RBF Neural Network Prediction Model Based on Particle Swarm Optimization for Internet-Based Teleoperation

For Internet based real-time teleoperation systems, the exact prediction of round trip timedelay (RTT) can have great importance on teleoperation systems performance. In order to solve Internet delay prediction problem, this paper proposes an improved radial basis function (RBF) neural network prediction model. In this model, which is different from other traditional prediction models, is that local particle swarm optimization algorithm is used to adjust RBF network parameters and binary particle swarm optimization algorithm is used to adjust structure of RBF model. Based on this idea, we propose the improved RBF neural network prediction model, and we use this model to make prediction of Internet delay. The experiment result shows that this model is effective.

[1]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Tao Wang,et al.  A hybrid optimization-based recurrent neural network for real-time data prediction , 2013, Neurocomputing.

[3]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  Bi Houjie,et al.  Differential AR algorithm for packet delay prediction , 2006 .

[5]  David L. Mills,et al.  Jitter-based delay-boundary prediction of wide-area networks , 2001, TNET.

[6]  Haralambos Sarimveis,et al.  A Radial Basis Function network training algorithm using a non-symmetric partition of the input space - Application to a Model Predictive Control configuration , 2011, Adv. Eng. Softw..

[7]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[8]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[9]  Hao Yu,et al.  Fast and Efficient Second-Order Method for Training Radial Basis Function Networks , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Haralambos Sarimveis,et al.  Radial Basis Function Network Training Using a Nonsymmetric Partition of the Input Space and Particle Swarm Optimization , 2013, IEEE Transactions on Neural Networks and Learning Systems.