Hybrid Lateral Aerodynamic Modeling Based on WNN and Kernel Principal Components Feature Extraction

Xu Sheng Gan; Hao Lin Cui; Ya Rong Wu; Yue Bo Meng

doi:10.4028/www.scientific.net/AMR.763.242

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 763Hybrid Lateral Aerodynamic Modeling Based on WNN...

Hybrid Lateral Aerodynamic Modeling Based on WNN and Kernel Principal Components Feature Extraction

Abstract:

In order to better describe the dynamic characteristics of aircraft through aerodynamic modeling, a Wavelet Neural Network (WNN) aerodynamic modeling method based on Kernel Principal Components Analysis (KPCA) is proposed. Firstly, the training samples are used to execute KPCA for extracting basic features of samples, and then using the extracted basic features, WNN aerodynamic model was established. The simulation result shows that, the modeling ability of the method proposed is better than that of another 3 methods. It can easily determine of model parameters. This enables it to be effective and feasible to establish the aerodynamic modeling for aircraft.

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Periodical:

Advanced Materials Research (Volume 763)

Pages:

242-245

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.763.242

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Online since:

September 2013

Authors:

Xu Sheng Gan, Hao Lin Cui, Ya Rong Wu, Yue Bo Meng

Keywords:

Aerodynamic Modeling, Feature Extraction, Kernel Principal Components Analysis, Wavelet Neural Network (WNN)

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References

[1] X. S. Gan, J. S. Duanmu, W. Cong. SSUKF-WNN algorithm and its applications in aerodynamic modeling of flight vehicles. Control and Decision, 26 (2) , (2011), 187-190.

Google Scholar

[2] Y. B. Meng, J. H. Zou, X. S. Gan, et al., Research on WNN aerodynamic modeling from flight data based on improved PSO algorithm. Neurocomputing, 83, (2012), 212-221.

DOI: 10.1016/j.neucom.2011.12.015

Google Scholar

[3] Y. L. Fan, P. Li, Z. H. Song. KPCA based on feature samples for fault detection. Control and Decision, 20(12) , (2005), 1415-1422.

Google Scholar

[4] Q. H. Zhang, A. Benveniste. Wavelet network. IEEE Transactions on Neural Networks, 3(6), (1992), 889-898.

Google Scholar

[5] R. V. Jategaonkar. Flight vehicle system identification: a time domain methodology. Progress in Astronautics and Aeronautics Series, 1st Edition, AIAA, (2006).

Google Scholar