A novel machine learning based method of combined dynamic environment prediction

Mathematical Problems in Engineering

Volumn 2013, Issue , 2013, Pages

  Mao, Wentao ^a,b   Yan, Guirong ^b   Dong, Longlei ^b  

^a HENAN NORMAL UNIVERSITY   (China)

^b XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENT BOUNDARY CONDITION; DYNAMIC ENVIRONMENTS; DYNAMIC SIMILARITIES; EXTREME LEARNING MACHINE; LEARNING-BASED METHODS; PRACTICAL ENGINEERING; STRUCTURAL VIBRATIONS; WHITE NOISE EXCITATION;

BOUNDARY CONDITIONS; FORECASTING; LEARNING SYSTEMS; MIMO SYSTEMS; REGRESSION ANALYSIS; STRUCTURAL DYNAMICS;

LEARNING ALGORITHMS;

EID: 84879303199     PISSN: 1024123X     EISSN: 15635147     Source Type: Journal    
DOI: 10.1155/2013/141849     Document Type: Article

References (20)

1. Barnoski R. L., Piersol A. G., van der Laan W. F., Summary of random vibration prediction procedures. NASA 1969 CR-1302
2. Barrett R. E., Techniques for predicting localized vibratory environments of rocket vehicles. NASA 1963 TN-l836
3. Franken P. A., Sound-induced vibrations of cylindrical vehicles. Journal of the Acoustical Society of America 1962 34 4 453 454
4. Szücs E., Similitude and Modeling 1980 New York, NY, USA Elsevier Scientific Publishing Co.
5. Singhatanadgid P., Na Songkhla A., An experimental investigation into the use of scaling laws for predicting vibration responses of rectangular thin plates. Journal of Sound and Vibration 2008 311 1-2 314 327 2-s2.0-38349154877 10.1016/j.jsv.2007.09.006
6. Rezaeepazhand J., Simitses G. J., Starnes J. H., Design of scaled down models for predicting shell vibration response. Journal of Sound and Vibration 1996 195 2 301 311 2-s2.0-0030213041 10.1006/jsvi.1996.0423
7. Lyon R. H., De Jong R. G., Theory and Applications of Statistical Energy Analysis 1995 Boston, Mass, USA Butterworth-Heinemann
8. Bishop C. M., Pattern Recognition and Machine Learning 2006 New York, NY, USA Springer xx+738 10.1007/978-0-387-45528-0 MR2247587 ZBL1152.92317
9. Huang G. B., Zhu Q. Y., Siew C. K., Extreme learning machine: theory and applications. Neurocomputing 2006 70 1-3 489 501 2-s2.0-33745903481 10.1016/j.neucom.2005.12.126
10. Chen F. L., Ou T. Y., Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry. Expert Systems with Applications 2011 38 3 1336 1345 2-s2.0-78049526620 10.1016/j.eswa.2010.07.014
11. Zhang X., Wang H. L., Selective forgetting extreme learning machine and its application to time series prediction Acta Physica Sinica 2011 60 8 080504
12. Pérez-Cruz F., Camps-Valls G., Soria-Olivas E., Multi-dimensional function approximation and regression estimation. Lecture Notes in Computer Science 2002 2415/2002 757 762
13. Sánchez-Fernández M., de-Prado-Cumplido M., Arenas-García J., Pérez-Cruz F., SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems. IEEE Transactions on Signal Processing 2004 52 8 2298 2307 10.1109/TSP.2004.831028 MR2085589
14. Schölkopf B., Smola A. J., Learning with Kernels 2002 MIT Press
15. Huang G. B., Babri H. A., Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Transactions on Neural Networks 1998 9 1 224 229 2-s2.0-0031673055
16. Huang G. B., Chen L., Siew C. K., Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 2006 17 4 879 892 2-s2.0-33745918399 10.1109/TNN.2006.875977
17. Huang G. B., Wang D. H., Lan Y., Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics 2011 2 2 107 122 2-s2.0-79958178274 10.1007/s13042-011-0019-y
18. Mao W., Hu D., Yan G., A new SVM regression approach for mechanical load identification. International Journal of Applied Electromagnetics and Mechanics 2010 33 3-4 1001 1008 2-s2.0-78650397012 10.3233/JAE-2010-1213
19. Hu D. K., Mao W. T., Zhao J., Yan G. R., Application of LSSVM-PSO to load identification in frequency domain. Proceedings of Artificial Intelligence and Computational Intelligence 2009 Shanghai, China 231 240
20. Hu D. K., Han F., Yan G. R., Modeling on the cylindrical shell vibration test system. Proceedings of the LMS China User Conference 2008 Xi'an, China