High accuracy behavior prediction of non-linear dynamic system with signal separation and support vector regression

Abstract

The behavior prediction of non-linear dynamic system is a challenging problem, especially when the system includes many independent subsystems. The observations from the complex dynamic system are the result of the interaction of multiple dynamic subsystems, which results in a loss of predictability. In this paper, signal separation technique is adopted to separate the observations of complex non-linear dynamic system in order to improve its predictability. And then local support vector regression technique is used to model the separated observations and make prediction. Finally, the prediction results are remixed as the original observation prediction or the behavior prediction of the complex non-linear dynamic system. The experimental results show that the proposed method improves the prediction accuracy substantially, which proves that signal separation can improve the predictability of complex non-linear dynamic system.

Keywords

Non-linear dynamic system signal separation support vector regression

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References

Povinelli

R.J.

and Feng

, A new temporal pattern identification method for characterization and prediction of complex time series events, IEEE Transactions on Knowledge and Data Engineering 15(2) (2003), 339–352.

Zhou

Song

and Liu

, Medium term load forecasting for chaotic neural networks based on the Euclidean distance, Journal of Computational Methods in Sciences and Engineering 16(3) (2016), 493–503.

Rafsanjani

M.K.

and Samareh

, Chaotic time series prediction by artificial neural networks, Journal of Computational Methods in Sciences and Engineering 16(3) (2016), 599–615.

Potter

Venayagamoorthy

G.K.

and Kosbar

, RNN based MIMO channel prediction, Signal Processing 90 (2010), 440–450.

Gorriz

J.M.

et al., Time series prediction using ICA algorithms, IEEE International Workshop on Intelligent Data Acquisition & Advanced Computing Systems: Technology & Applications (5) (2003), 226–230.

Lee

et al., Noisy time series prediction using M-estimator based robust radial basis function neural networks with growing and pruning techniques, Expert Systems with Applications 36(3) (2009), 4717–4724.

et al., Travel-time prediction with support vector regression, IEEE Transaction on Intelligent Transportation Systems 5(4) (2004), 276–220.

Chen

et al., Time-series prediction using a local linear wavelet neural network, Neurocomputing 69 (2006), 449–465.

et al., An improved time series prediction by applying the layer-by-layer learning method to fir neural networks, Neural Networks 10(9) (1997), 1717–1729.

10.

Milidiu

et al., Time series forecasting through wavelets transformation and a mixture expert models, Neurocomputing 28 (1999), 145–146.

11.

Cao

, Support vector machines experts for time series forecasting, Neurocomputing 51 (2003), 321–339.

12.

Hyvarinen

Karhunen

and Oja

, Independent component analysis. Beijing: Publishing House of Electronics Industry, 2014.

13.

Lau

K.W.

and Wu

Q.H.

, Local prediction of nonlinear time series using support vector regression, Pattern Recognition 41 (2008), 1539–1547.

14.

Simmons

G.F.

. Differential Equations, New York: McGraw-Hill, 1972.

15.

Chiu

S.H.

and Lu

C.P.

, Noise separation of the yarn tension signal on twister using FastICA, Mechanical Systems and Signal Processing 19 (2005), 1326–1336.

16.

Robinson

R.C.

, An Introduction to Dynamical Systems: Continuous and Discreet. Prentice Hall, 2004.

17.

Wang

Zhang

and Xu

H.C.

, Local prediction of Complex Time Series based on Support Vector Machine and Differential Evolution algorithm, The Second International Symposium on Computational Intelligence and Design 2 (2009), 425–428.

18.

Time Series Prediction group [EB/OL], http://www.cis.hut.fi/projects/tsp/?page=Timeseries, 2006-05-08.