Detection of Fractional Data Based on Hilbert-Huang Transformation

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 308-310)

Edited by:

Jian Gao

Pages:

1546-1550

DOI:

10.4028/www.scientific.net/AMR.308-310.1546

Citation:

C. J. Li and J. P. Wan, "Detection of Fractional Data Based on Hilbert-Huang Transformation", Advanced Materials Research, Vols. 308-310, pp. 1546-1550, 2011

Online since:

August 2011

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Price:

$35.00

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