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Detection of Fractional Data Based on Hilbert-Huang Transformation

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Abstract:

In many theoretical analysis and engineering application fields, fractional Brownian motions has proposed to be a valuable random excitation due to its' key self-similarity and fractal nature. And Hilbert-Huang transformation is counted as an effective tool to deal with nonlinear and non-stationary data. In this paper, we propose Hilbert-Huang transformation to process fractional data, then by verifying and differentiating the marginal spectrum or power spectrum of fractional data we formulate a stochastic detection scheme.

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Periodical:

Advanced Materials Research (Volumes 308-310)

Pages:

1546-1550

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.308-310.1546

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Online since:

August 2011

Authors:

Chu Jin Li, Jian Ping Wan

Keywords:

Fractional Brownian Motion, Hilbert-Huang Transformation (HHT), Stochastic Detection, Stochastic Modeling

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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