Prediction of Chaotic Time Series of RBF Neural Network Based on Particle Swarm Optimization

Baoxiang Du; Wei Xu; Bingbing Song; Qun Ding; Shu-Chuan Chu

doi:10.1007/978-3-319-07773-4_48

Prediction of Chaotic Time Series of RBF Neural Network Based on Particle Swarm Optimization

Baoxiang Du, Wei Xu, Bingbing Song, Qun Ding, Shu-Chuan Chu

Research output: Contribution to conference › Paper › peer-review

6 Citations (Scopus)

Abstract

Radial basis function (RBF) neural network has very good performance on prediction of chaotic time series, but the precision of prediction is great affected by embedding dimension and delay time of phase-space reconstruction in the process of predicting. Based on hereinbefore problems, we comprehensive optimize embedding dimension and delay time by particle swarm optimization, to get the optimal values of embedding dimension and delay time in RBF single-step and multi-step prediction models. In addition, we made single step and multi-step prediction to the Lorenz system by this method, the results show that the prediction accuracy of optimized prediction model is obvious improved.

Original language	English
Pages	489-497
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-319-07773-4_48
Publication status	Published - 1 Jan 2014
Event	The First Euro-China Conference on Intelligent Data Analysis and Applications - Shenzhen, China Duration: 13 Jun 2014 → 15 Jun 2014

Conference

Conference	The First Euro-China Conference on Intelligent Data Analysis and Applications
Country/Territory	China
City	Shenzhen
Period	13/06/14 → 15/06/14

Keywords

Neural network
Particle swarm optimization
Prediction
Radial basis function (RBF)

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10.1007/978-3-319-07773-4_48

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title = "Prediction of Chaotic Time Series of RBF Neural Network Based on Particle Swarm Optimization",

abstract = "Radial basis function (RBF) neural network has very good performance on prediction of chaotic time series, but the precision of prediction is great affected by embedding dimension and delay time of phase-space reconstruction in the process of predicting. Based on hereinbefore problems, we comprehensive optimize embedding dimension and delay time by particle swarm optimization, to get the optimal values of embedding dimension and delay time in RBF single-step and multi-step prediction models. In addition, we made single step and multi-step prediction to the Lorenz system by this method, the results show that the prediction accuracy of optimized prediction model is obvious improved.",

keywords = "Neural network, Particle swarm optimization, Prediction, Radial basis function (RBF)",

author = "Baoxiang Du and Wei Xu and Bingbing Song and Qun Ding and Shu-Chuan Chu",

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day = "1",

doi = "10.1007/978-3-319-07773-4_48",

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TY - CONF

T1 - Prediction of Chaotic Time Series of RBF Neural Network Based on Particle Swarm Optimization

AU - Du, Baoxiang

AU - Xu, Wei

AU - Song, Bingbing

AU - Ding, Qun

AU - Chu, Shu-Chuan

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Radial basis function (RBF) neural network has very good performance on prediction of chaotic time series, but the precision of prediction is great affected by embedding dimension and delay time of phase-space reconstruction in the process of predicting. Based on hereinbefore problems, we comprehensive optimize embedding dimension and delay time by particle swarm optimization, to get the optimal values of embedding dimension and delay time in RBF single-step and multi-step prediction models. In addition, we made single step and multi-step prediction to the Lorenz system by this method, the results show that the prediction accuracy of optimized prediction model is obvious improved.

AB - Radial basis function (RBF) neural network has very good performance on prediction of chaotic time series, but the precision of prediction is great affected by embedding dimension and delay time of phase-space reconstruction in the process of predicting. Based on hereinbefore problems, we comprehensive optimize embedding dimension and delay time by particle swarm optimization, to get the optimal values of embedding dimension and delay time in RBF single-step and multi-step prediction models. In addition, we made single step and multi-step prediction to the Lorenz system by this method, the results show that the prediction accuracy of optimized prediction model is obvious improved.

KW - Neural network

KW - Particle swarm optimization

KW - Prediction

KW - Radial basis function (RBF)

UR - http://www.scopus.com/inward/record.url?scp=84928964199&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-07773-4_48

DO - 10.1007/978-3-319-07773-4_48

M3 - Paper

SP - 489

EP - 497

T2 - The First Euro-China Conference on Intelligent Data Analysis and Applications

Y2 - 13 June 2014 through 15 June 2014

ER -

Prediction of Chaotic Time Series of RBF Neural Network Based on Particle Swarm Optimization

Abstract

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Keywords

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