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

被引：2
|
作者：
Li, Guodong [1 ]
Song, Zhixin [1 ]
机构：
[1] North China Elect Power Univ, Sch Control & Comp Engn, Beijing, Peoples R China
来源：
2014 SEVENTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2014), VOL 2 | 2014年
关键词：
component; RBF neural network; Particle swarm optimization; Internet delay prediction; NONSYMMETRIC PARTITION; INPUT SPACE; ALGORITHM;
D O I：
10.1109/ISCID.2014.57
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页数：4
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