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Multiscale analysis of complex time series; integration of chaos and random fractal theory, and beyond.


Multiscale analysis of complex time series; integration of chaos and random fractal theory, and beyond.

Ed. by Jianbo Gao et al.



352 pages




The authors (of the U. of Florida, Purdue U., and the firm BioSieve) present a unified treatment of the use of chaos theory and random fractal theory for data analysis and signal processing at a level suitable for a one-year graduate-level course for students of electrical engineering, computer science, mechanical engineering, mathematics, finance, population ecology, and other fields. Following the introduction and overview, chapters address probability theory and stochastic processes, Fourier analysis and wavelet multiresolution analysis, fractal geometry, self-similar stochastic processes, stable laws and Levy motions, long memory processes and structure-function-based multifractal analysis, multiplicative multifractals, stage-dependent multiplicative processes, models of power-law-type behavior, bifurcation theory, chaotic time series analysis, power-law sensitivity to initial conditions, and multiscale analysis by the scale-dependent Lyapunov exponent.

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Publication:SciTech Book News
Article Type:Brief Article
Date:Mar 1, 2008
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