Prediction for noisy nonlinear time series by echo state network based on dual estimation

Neurocomputing

Volumn 82, Issue , 2012, Pages 186-195

  Sheng, Chunyang ^a   Zhao, Jun ^a   Liu, Ying ^a   Wang, Wei ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Dual estimation; Echo state network; Kalman filter; Prediction; Time series

Indexed keywords

BLAST FURNACE GAS; DUAL ESTIMATION; ECHO STATE NETWORKS; INTERNAL STATE; MACKEY-GLASS TIME SERIES; NOISE ADDITION; NONLINEAR KALMAN FILTER; NONLINEAR TIME SERIES; PARAMETERS DETERMINATION; PREDICTION MODEL; REGRESSION MODEL;

BLAST FURNACE PRACTICE; BLAST FURNACES; ESTIMATION; FORECASTING; GLASS INDUSTRY; IRON AND STEEL PLANTS; KALMAN FILTERS; MATHEMATICAL MODELS; REGRESSION ANALYSIS; STEELMAKING; TIME SERIES;

NONLINEAR ANALYSIS;

ANALYTICAL PARAMETERS; ARTICLE; ARTIFICIAL NEURAL NETWORK; BLAST FURNACE GAS; ECHO STATE NETWORK; FILTER; GAS; GAS FLOW; IRON AND STEEL INDUSTRY; LEARNING STYLE; LINEAR SYSTEM; NOISY NONLINEAR TIME SERIES; NONLINEAR SYSTEM; PREDICTION; PRIORITY JOURNAL; STATISTICAL MODEL; TIME SERIES ANALYSIS; VALIDATION PROCESS;

EID: 84862788716     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.11.021     Document Type: Article

References (34)

1. Hornik K., Stinchcombe M., White H. Multilayer feed-forward networks are universal approximators. Neural Networks 1989, 2:359-366.
2. Jaeger H., Haass H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless telecommunication. Science 2004, 304:78-80.
3. Zhai F., Lin X., Yang Z. Financial time series prediction based on echo state network. 2010 Sixth International Conference on Natural Computation 2010, 3983-3987.
4. Wyffels F., Schrauwen B. A comparative study of reservoir computing strategies for monthly time series prediction. Neurocomputing 2010, 73:1958-1964.
5. Showkati H., Hejazi A.H., Elyasi S. Short term load forecasting using echo state networks. The 2010 International Joint Conference on Neural Networks 2010, 1-5.
6. Xia Y., Jelfs B., Van Hulle M.M. An augmented echo state network for nonlinear adaptive filtering of complex noncircular signals. IEEE Trans. Neural Networks 2010, 22:1-10.
7. Seth S., Ozturk M.C., Principe J.C. Signal processing with echo state networks in the complex domain. 2007 IEEE Workshop on Machine Learning for Signal Processing 2007, 408-412.
8. P.G. Ploger, A. Arghir, T. Gunther, Echo state networks for mobile robot modeling and control, in: Robot Soccer World Cup 3020, 2003, pp. 157-168.
9. Jaeger H. Reservoir riddles: suggesting for echo state network research. 2005 IEEE International Joint Conference on Neural Networks 2005, 1460-1462.
10. Liu Y., Zhao J., Wang W. Improved echo state network based on data-driven and its application in prediction of blast furnace gas output. Acta Automatica Sin. 2009, 35:731-738.
11. Xue Y., Yang L., Haykin S. Decoupled echo state networks with lateral inhibition. Neural Networks 2007, 20:365-376.
12. Steil J.J. Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. Neural Networks 2007, 20:353-364.
13. Song Q., Feng Z., Lei M. Stable training method for echo state networks with output feedback. 2010 International Conference on Networking, Sensing and Control 2010, 159-164.
14. Han M., Mu D. Multi-reservoir echo state network with sparse Bayesian learning. Adv. Neural Networks 2010, 450-456.
15. Zhang B., Wang Y. Echo state networks with decoupled reservoir states. 2008 International Workshop on Machine Learning for Signal processing 2008, 444-449.
16. Wan E.A., Nelson A.T. Dual extended Kalman filter methods. Kalman Filtering and Neural Networks 2001, 123-174. Wiley. S. Haykin (Ed.).
17. Mehra R. A comparison of several non-linear filters for re-entry vehicle tracking. IEEE Trans. Autom. Control 1971, 16:307-319.
18. Athans M., Wsihner R.P., Bertolini A. Suboptimal state estimation for continuous-time nonlinear systems from discrete noise measurements. IEEE Trans. Autom. Control 1968, 13:504-514.
19. Schei T.S. A finite difference method for linearizing in nonlinear estimation algorithms. Automatica 1997, 33:2051-2058.
20. Julier S., Uhlmann J. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans. Autom. Control 2000, 45:477-482.
21. S. Julier, J. Uhlmann, Unscented filtering and nonlinear estimation, in: IEEE Invited Paper, Proceedings of the IEEE, vol. 92, 2004, pp. 401-422.
22. Arasaratnam I., Haykin S. Cubature Kalman filters. IEEE Trans. Autom. Control 2009, 54:1254-1269.
23. Arasaratnam I., Haykin S. Nonlinear Bayesian filters for training recurrent neural networks. Advances in Artificial Intelligence 2008, 12-33. Springer. A. Gelbukh, E. Morales (Eds.).
24. Wang Z., Liu X., Liu Y., Liang J., Vinciotti V. An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2009, 6:410-419.
25. Yang H., Li J., Ding F. A neural network learning algorithm of chemical process modeling based on the extended Kalman filter. Neurocomputing 2007, 70:625-632.
26. Simon D. Training radial basis neural networks with the extended Kalman filter. Neurocomputing 2002, 48:455-475.
27. Wan E.A., Merwe R. The unscented Kalman filter. Kalman Filtering and Neural Networks 2001, 221-280. Wiley. S. Haykin (Ed.).
28. Wang Z., Yang F., Ho D.W.C., Swift S., Tucker A., Liu X. Stochastic dynamic modeling of short gene expression time series data. IEEE Trans. NanoBiosci. 2008, 7:44-55.
29. Mackey M.C., Glass L. Oscillation and chaos in physiological control systems. Science 1977, 197:287-289.
30. Venayagamoorthy G., Shishir B. Effects of spectral radius and settling time in the performance of echo state networks. Neural Networks 2009, 22:861-863.
31. Zeng N., Wang Z., Li Y., Du M., Liu X. Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering. IEEE Trans. Biomed. Eng. 2011, 58:1959-1966.
32. Shen B., Wang Z., Hung Y.S., Chesi G. Distributed H-infinity filtering for polynomial nonlinear stochastic systems in sensor networks. IEEE Trans. Ind. Electron. 2011, 58:1971-1979.
33. Liang J., Wang Z., Liu X. Distributed state estimation for discrete-time sensor networks with randomly varying nonlinearities and missing measurements. IEEE Trans. Neural Networks 2011, 22:486-496.
34. Shen B., Wang Z., Liu X. Bounded H-infinity synchronization and state estimation for discrete time-varying stochastic complex networks over a finite-horizon. IEEE Trans. Neural Networks 2011, 22:145-157.