Variable selection in identification of a high dimensional nonlinear non-parametric system
详细信息 查看全文
- 作者：Er-Wei Bai (1) (2)
Wenxiao Zhao (3)
Weixing Zheng (4)
1. Department of Electrical and Computer Engineering ; University of Iowa ; Iowa City ; Iowa ; 52242 ; USA
2. School of Electronics ; Electrical Engineering and Computer Science ; Queen鈥檚 University ; Belfast ; UK
3. Key Laboratory of Systems and Control ; Academy of Mathematics and Systems Science ; National Center for Mathematics and Interdisciplinary Sciences ; Chinese Academy of Sciences ; Beijing ; 100190 ; China
4. School of Computing ; Engineering and Mathematics ; University of Western Sydney ; Penrith ; NSW ; 2751 ; Australia - 关键词：System identification ; variable selection ; nonlinear non ; parametric system ; curse of dimensionality
- 刊名：Journal of Control Theory and Applications
- 出版年：2015
- 出版时间：February 2015
- 年：2015
- 卷：13
- 期：1
- 页码：1-16
- 全文大小：613 KB
- 参考文献：1. Li, K, Peng, J (2007) Neuro input selection 鈥?a fast model based approach. Neurocomputing 70: pp. 762-769 2006.10.011" target="_blank" title="It opens in new window">CrossRef
2. Li, K, Peng, J, Bai, E W (2006) A two-stage algorithm for identification of nonlinear dynamic systems. Automatica 42: pp. 1189-1197 2006.03.004" target="_blank" title="It opens in new window">CrossRef
3. Peng, J, Ferguson, S, Rafferty, K (2011) An efficient feature selection method for mobile devices with application to activity recognition. Neurocomputing 74: pp. 3543-3552 2011.06.023" target="_blank" title="It opens in new window">CrossRef
4. Lobato, I N, Nankervis, J, Savin, N (2002) Testing for zero autocorrelation in the presence of statistical dependence. Econometric Theory 18: pp. 730-743 266466602183083" target="_blank" title="It opens in new window">CrossRef
5. S枚derstr枚m, T, Stoica, P (1989) System Identification. Prentice Hall, New York
6. Velasco, C, Lobato, I (2004) A simple and general test for white noise. Proceedings of Econometric Society 2004 Latin American Meetings, Santiago, Chile. pp. 112-113
7. Su, S, Yang, F (2002) On the dynamical modeling with neural fuzzy networks. IEEE Transactions on Neural Network 13: pp. 1548-1553 2002.804313" target="_blank" title="It opens in new window">CrossRef
8. Bomerger, J D, Seborg, D E (1998) Determination of model order for NARX models directly from input-output data. Journal of Process Control 8: pp. 459-568 24(98)00013-4" target="_blank" title="It opens in new window">CrossRef
9. Zhao, W, Chen, H-F, Bai, E W (2015) Kernel-based local order estimation of nonlinear non-parametric systems. Automatica 51: pp. 243-254 2014.10.069" target="_blank" title="It opens in new window">CrossRef
10. Pillonetto, G, Quang, M, Chiuso, A (2011) A new kernel-based approach for nonlinear system identification. IEEE Transactions on Automatic Control 56: pp. 2825-2840 2011.2131830" target="_blank" title="It opens in new window">CrossRef
11. Hong, X, Mitchell, R T, Chen, S (2008) Model selection approaches for nonlinear system identification: a review. International Journal of Systems Science 39: pp. 925-949 207720802083018" target="_blank" title="It opens in new window">CrossRef
12. X. He, H. Asada. A new method for identifying orders of input-output models for nonlinear dynamic systems. / Proceedings of the American Control Conference, San Francisco, CA: IEEE, 2003: 2520 - 2523.
13. Kennel, M B, Brown, M R, Abarbanel, H (1992) Determining embedding dimension for phase-space reconstruction using geometrical construction. Physical Review A 45: pp. 3403-3411 CrossRef
14. Cao, L (1997) Practical method for determining the minimum embedding dimension input-output models for nonlinear dynamic systems. Physica D: Nonlinear Phenomena 110: pp. 43-50 2789(97)00118-8" target="_blank" title="It opens in new window">CrossRef
15. Roweis, S, Saul, L (2000) Nonlinear dimensionality reduction by local linear embedding. Science 290: pp. 2323-2326 26/science.290.5500.2323" target="_blank" title="It opens in new window">CrossRef
16. Achlioptas, D (2003) Database friendly random projections: Johnson-Lindenstrass with binary coins. Journal of Computer and System Sciences 66: pp. 671-687 22-0000(03)00025-4" target="_blank" title="It opens in new window">CrossRef
17. Ohlsson, H, Roll, J, Glad, T (2007) Using manifold learning for nonlinear system identification. Proceedings of the 7th IFAC Symposium on Nonlinear Control Systems. Elsevier, Pretoria, South Africa, pp. 170-175
18. Roll, J, Nazin, A, Ljung, L (2005) Nonlinear system identification via direct weight optimization. Automatica 41: pp. 475-490 2004.11.010" target="_blank" title="It opens in new window">CrossRef
19. Kocijan, J, Girard, A, Banko, B (2003) Dynamic systems identification with Gaussian process. Mathematical and Computer Modelling of Dynamical Systems 11: pp. 411-424 CrossRef
20. Knight, K, Fu, W (2000) Asymptotics for Lasso-type estimators. The Annals of Statistics 28: pp. 1356-1378 214/aos/1015957397" target="_blank" title="It opens in new window">CrossRef
21. Zou, H (2006) The adaptive Lasso and its oracle properties. Journal of American Statistical Association 101: pp. 1418-1429 214506000000735" target="_blank" title="It opens in new window">CrossRef
22. Arribas-Gil, A, Bertin, K, Meza, C (2013) LASSO-type estimators for semiparametric nonlinear mixed-effects model estimation. Statistics and Computing 24: pp. 443-460 222-013-9380-x" target="_blank" title="It opens in new window">CrossRef
23. Bai, E W, Li, K, Zhao, W (2014) Kernel based approaches to local nonlinear non-parametric variable selection. Automatica 50: pp. 100-113 2013.10.010" target="_blank" title="It opens in new window">CrossRef
24. Bai, E W (2010) Non-parametric nonlinear system identification: an asymptotic minimum mean squared error estimator. IEEE Transactions on Automatic Control 55: pp. 1615-1626 2010.2042343" target="_blank" title="It opens in new window">CrossRef
25. Mao, K, Billings, S A (2006) Variable selection in nonlinear system modeling. Mechanical Systems and Signal Processing 13: pp. 351-366 CrossRef
26. Mosci, R, Rosasco, L, Santoro, M (2012) Is there sparsity beyond additive models?. Proceedings of the 16th IFAC Symposium on Systems Identification. Elsevier, Brussels, Belgium, pp. 971-976
27. Bai, E W, Liu, Y (2007) Recursive direct weight optimization in nonlinear system identification: a minimal probability approach. IEEE Transactions on Automatic Control 52: pp. 1218-1231 2007.900826" target="_blank" title="It opens in new window">CrossRef
28. Zhao, W, Chen, H-F, Bai, E W (2015) Variable selection for NARX systems in first approximation.
29. Zhao, W, Zheng, W, Bai, E W (2013) A recursive local linear estimator for identification of nonlinear ARX systems: asymptotical convergence and applications. IEEE Transactions on Automatic Control 58: pp. 3054-3069 2013.2273283" target="_blank" title="It opens in new window">CrossRef
30. Peduzzi, P (1980) A stepwise variable selection procedure for nonlinear regression methods. Biometrics 36: pp. 510-516 2307/2530219" target="_blank" title="It opens in new window">CrossRef
31. Box, G E P, Pierce, D (1970) Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of American Statistical Association 65: pp. 1509-1526 21459.1970.10481180" target="_blank" title="It opens in new window">CrossRef - 刊物类别：Computer Science
- 刊物主题：Control Structures and Microprogramming
Chinese Library of Science - 出版者：South China University of Technology and Academy of Mathematics and Systems Science, CAS
- ISSN：1993-0623
文摘
The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.