Detection of Fractional Data Based on Hilbert-Huang Transformation
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.
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