A measurement fusion method for nonlinear system identification using a cooperative learning algorithm

Neural Computation

Volumn 19, Issue 6, 2007, Pages 1589-1632

  Xia, Youshen ^a   Kamel, Mohamed S ^b  

^a FUZHOU UNIVERSITY   (China)

^b UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANIMAL; ARTICLE; COOPERATION; HUMAN; LEARNING; NONLINEAR SYSTEM; PHYSIOLOGY;

ALGORITHMS; ANIMALS; COOPERATIVE BEHAVIOR; HUMANS; LEARNING; NONLINEAR DYNAMICS;

ERRORS; MEAN SQUARE ERROR; SUPPORT VECTOR MACHINES;

COOPERATIVE LEARNING; FUSION METHODS; FUSION TECHNIQUES; LEAST SQUARE SUPPORT VECTOR MACHINES; MEASUREMENT FUSION; MEASUREMENT FUSION FUNCTIONS; NON-LINEAR SYSTEM IDENTIFICATION; OBSERVED DATA; OPTIMAL FUSION; OPTIMAL MEASUREMENTS;

LEARNING ALGORITHMS;

EID: 34249694108     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2007.19.6.1589     Document Type: Article

References (42)

1. Abed-Meraim, K., Qui, W. Z., & Hua, Y. B. (1997). Blind system identification, Proceedings of the IEEE, 85, 1310-1322.
2. Bazaraa, M. S., Sherali, H. D., & Shetty, C. M. (1993). Nonlinear Programming: Theory and algorithms, Hoboken, NJ: Wiley.
3. Chen, L. J., & N arendra, K. S. (2004). Identification and control of a nonlinear discrete-time system based on its linearization: A unified framework, IEEE Transactions on Neural Networks, 15, 663-673.
4. Chen, S., & Billings, S. A. (1992). Neural networks for nonlinear dynamic system modelling and identification, international Journal of Control, 56, 319-346.
5. Cressie, N., & Majure, J. J. (1997). Spatial-temporal statistical modeling of live-stock waste in streams. Journal of Agricultural, Biological, and Environmental Statistics, 2, 24-47.
6. Drosopoulos, A. (1994). Description of the. OHGR database (Tech. Note 94-14). Ottawa, Ont.: Defence Research Establishment.
7. Fan, J., & Gijbels, I. (1996). Local polynomial modelling and its application, London: Chapman and Hall.
8. Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85, 6-23.
9. Härdle, W. (1990). Applied nonparametric regression. Cambridge: Cambridge University Press.
10. Haykin, S., & Puthusserypady, S. (1997). Chaotic dynamics of sea clutter. Chaos, 7, 777-802.
11. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359-366.
12. Hsu, C.-W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification (Tech. Rep.). Taiwan: Department of Computer Science and Information Engineering, National Taiwan University.
13. Kadirkamanathan, V., & Niranjan, M. (1993). A function estimation. Neural Computation, 5, 954-975.
14. Kinderlehrer, D., & Stampcchia, G. (1980). An introduction to variational inequalities and their applications. New York: Academic Press.
15. Koukoulas, P., & Kalouptsidis, N. (2000). Second-order Volterra system identification. IEEE Transactions on Signal Processing, 48, 3574-3577.
16. Lee, J., & Mathews, V. J. (1993). A fast recursive least squares adaptive second-order Volterra filter and its performance analysis. IEEE Trans. Signal Processing, 41, 1087-1097.
17. Leung, H., Hennessey, G., & Drosopoulos, A. (2000). Signal detection using the radial basis function coupled map lattice. IEEE Transactions on Neural Networks, 11, 1133-1151.
18. Ljung, L. (1999). System identification: Theory for the user (2nd ed.), Upper Saddle River, NJ: Prentice-Hall.
19. Lo, J. T. (1994). Synthetic approach to optimal filtering. IEEE Transactions on Neural Networks, 5, 803-811.
20. Lu, S., Ju, K. H., & Chon, K. H. (2001). A new algorithm for linear and nonlinear ARMA model parameter estimation using affine geometry. IEEE Transactions on Biomedical Engineering, 48, 1116-1122.
21. Mardia, K. V., Coodall, C., Redfern, E. J., & Alsonso, F. J. (1998). The kriged Kalman filter. Test, 7, 217-285.
22. Mhaskar, H. N., & Hahm, N. (1997). Neural networks for functional approximation and system identification. Neural Computation, 9, 143-159.
23. Mukherjee, S., Osuna, E., & Girosi, F. (1997). Nonlinear prediction of chaotic time series using support vector machines. Proc. of IEEE NNSP (pp. 24-26). Piscataway, NJ: IEEE Press.
24. Muller, K. R., Smola, A. J., Ratsch, G., Schölkopf, B. S., Kohlmorgen, J., & Vapnik, V. (1997). Predicting time series with support vector machines: Proceedings of ICANN. Berlin: Springer.
25. Narendra, K. S., & Li, S. M. (1996). Neural networks in control systems. In P. Smolen-sky, M. C. Mozer, & D. E. Rumelhart (Eds.), Mathematical perspectives on neural networks, (pp. 374-394). Mahwah, NJ: Erlbaum.
26. Rojo-Alvarez, J. L., Martinez-Ramon, M., Prado-Cumplido, M. de, Artes-Rodriguez, A., & Figueiras-Vidal, A. R. (2004). Support vector method for robust ARMA system identification. IEEE Transactions on Signal Processing, 52, 155-164.
27. Roll, J., Nazin A., & Ljung, L. (2005). Nonlinear system identification via direct weight optimization. Automatica, 41, 475-490.
28. Sahu, S. K., & Mardia, K. V. (1999). A Bayesian kriged Kalman model for short-term forecasting of air pollution levels. Applied Statistics, 54, 223-244.
29. Schoukens, J., Nemeth, J. G., Crama, P., Rolain, Y., & Pintelon, R. (2003). Fast approximate identification of nonlinear systems. Automatica, 39, 1267-1274.
30. Sjoberg, J., Zhang, Q. H., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P. Y., Hjalmarsson, H., & Juditsky, A. (1995). Nonlinear black-box modeling in system identification: A unified overview. Automatica, 31, 1691-1724.
31. Smola, A. J., & Schölkopf, B. S. (1998). On a Kernel-based method for pattern recognition, regression, approximation, and operator inversion. Algorithmica, 22, 211-231.
32. Suykens, J. A. K., Gestel, T. V., Brabanter, J. D., Moor, B. D., & Vandewalle, J. (2002). Least squares support vector machines. Singapore: World Scientific.
33. Suykens, J. A. K., Brabanter, J., Lukas, L., & Vandewalle, J. (2002). Weighted least squares support vector machines: Robustness and sparse approximation. Neuro-computing, 48, 85-105.
34. Tekalp, A. M. (1995). Digital video processing. Upper Saddle River, NJ: Prentice Hall.
35. Tekalp, A. M., Kaufman, H., Woods, J. W. (1985). Fast recursive estimation of the parameters of a space-varying autoregressive image model. IEEE Transactions on Acoustics, Speech, and Signal Processing, 33, 469-472.
36. Varshney, P. K. (1997). Multisensor data fusion. Journal of Electronics and Communication Engineering, 9, 245-253.
37. Wikle, C. K., & Cressie, N. (1999). A dimension-reduced approach to space-time Kalman filtering. Biometrika, 86, 815-829.
38. Xia, Y.S. (1996). A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks, 7, 1544-1547.
39. Xia, Y. S., & Leung, H. (2006). Spatial temporal prediction based on optimal fusion. IEEE Transactions on Neural Networks, 17, 975-988.
40. Xia, Y. S., Leung, H., & Bossé, E. (2002). Neural data fusion algorithms based on a linearly constrained least square method. IEEE Transactions on Neural Networks, 13, 320-329.
41. Xia, Y. S., Leung, H., & Chan, H. (2006). A prediction fusion method for reconstructing spatial temporal dynamics using support vector machines. IEEE Transactions on Circuits and Systems - Part II Express Briefs, 53, 62-66.
42. Zheng, W. X. (1999). A least-squares based method for autoregressive signals in the presence of noise. IEEE Transactions on Circuits and Systems II-Analog and Digital Signal Processing, 46, 81-85.