Time-Frequency Analysis Based on PNN for Nonstationary Random Vibration of Spacecraft

DOI

10.2991/iske.2007.75How to use a DOI?

Keywords

time-varying parameter; power spectrum; process neural network; EMD

Abstract

In view of the disadvantages of the traditional time-varying algorithm about nonstationary random vibration signal of a spacecraft with close spaced modal frequency. A process neural network (PNN) based on the empirical mode decomposition (EMD) method is put forward. First, the EMD method is utilized to decompose the signal into several intrinsic mode functions (IMFs). Then for each IMF, The PNN is established and time-varying auto-spectral density is obtained. Finally, the time-varying auto-spectral density of the signal can be reconstituted by linear superposing. The example analyzed results suggest effectiveness of this new method in time-frequency analysis.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hai Yang
AU  - Wei Cheng
AU  - Hong Zhu
AU  - None None
PY  - 2007/10
DA  - 2007/10
TI  - Time-Frequency Analysis Based on PNN for Nonstationary Random Vibration of Spacecraft
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 447
EP  - 453
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.75
DO  - 10.2991/iske.2007.75
ID  - Yang2007/10
ER  -