Optimal Classification of Hypersonic Inlet Start/Unstart Based on Manifold Learning

Ri Sheng Zheng; Jun Tao Chang; Hui Xin He; Fu Chen

doi:10.4028/www.scientific.net/AMM.274.200

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 274Optimal Classification of Hypersonic Inlet...

Optimal Classification of Hypersonic Inlet Start/Unstart Based on Manifold Learning

Abstract:

Inlet start/unstart detection has been the focus of researching hypersonic inlet, the operation mode of the inlet detection is the prerequisite for the unstart protection control of scramjet. Actually, due to computational complexity and high dimension discrete experimental data, all of these factors are against for the classification of real-time data. To solve this problem, firstly, the 2-D wind tunnel experiment is carried out, inlet start/unstart experiment phenomenon are analyzed; Secondly, isomap algorithm is introduced to reduce high dimensional data , the optimal classification method were obtained with the weighted embedded manifold learning algorithm, At last the superiority of the classification criterion is verified by decision tree algorithm.

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Periodical:

Applied Mechanics and Materials (Volume 274)

Pages:

200-203

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.274.200

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Online since:

January 2013

Authors:

Ri Sheng Zheng, Jun Tao Chang, Hui Xin He, Fu Chen

Keywords:

Decision Tree, Inlet Start/Unstart, Manifold Learning, Scramjet

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References

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