Accession Number : ADA297343
Title : Analysis of Non-Gaussian Processes Using the Wiener Model of Discrete Nonlinear Systems.
Descriptive Note : Doctoral thesis,
Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA
Personal Author(s) : Hashad, Atalla I.
PDF Url : ADA297343
Report Date : DEC 1994
Pagination or Media Count : 200
Abstract : Fundamental results developed by Wiener in the 1950's are combined with new work in the area of higher-order statistics to develop and explore a general model for nonlinear stochastic processes. The Wiener model is developed for discrete nonlinear systems and its orthogonality properties are analyzed to characterize its output statistics. An efficient structured procedure for computing the order statistics of the model output is formulated in both the time and frequency domains. Explicit formulas that exploit the structure of the Wiener model are given for computing the cumulants and polyspectra. A necessary condition for a discrete random process to be representable by the Wiener model is discussed. A computationally efficient procedure is given for matching the model output cumulants to estimated cumulants for a given process by minimizing the squared magnitude of the error. Examples of applying this procedure to given sets of data are presented. (AN)
Descriptors : *MATHEMATICAL MODELS, *STOCHASTIC PROCESSES, *NONLINEAR SYSTEMS, ALGORITHMS, SIGNAL PROCESSING, OPTIMIZATION, COMPUTATIONS, TRANSFER FUNCTIONS, PARAMETERS, RANDOM VARIABLES, TIME SERIES ANALYSIS, AUTOCORRELATION, WHITE NOISE, KALMAN FILTERING, EFFICIENCY, THESES, POWER SPECTRA, PROBABILITY DENSITY FUNCTIONS, KERNEL FUNCTIONS, POLYNOMIALS, DISCRETE DISTRIBUTION, SYSTEMS ANALYSIS, ORDER STATISTICS, STATISTICAL PROCESSES, METHOD OF MOMENTS, TIME DOMAIN, FREQUENCY DOMAIN, ORTHOGONALITY, CROSS CORRELATION, VOLTERRA EQUATIONS, LAGUERRE FUNCTIONS.
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE