3 edition of Shaping filter models for nonstationary random processes found in the catalog.
Shaping filter models for nonstationary random processes
Lane H. Brandenburg
in [New York]
Written in English
|Statement||[by] Lane H. Brandenburg.|
|LC Classifications||QA402 .B65|
|The Physical Object|
|Pagination||vi, 145 l.|
|Number of Pages||145|
|LC Control Number||81462044|
Nonstationary Stochastic Models of Earthquake Motions by Mohammad Amin, Alfredo H.-S. Ang, Serial Information: Journal of the Engineering Mechanics Division, , Vol. 94, Issue 2, Pg. Document Type: Journal Paper Abstract: Eight strong-motion accelerograms recorded on firm ground and at moderate epicentral distances are studied for the purpose of developing a reasonable stochastic. where n and k are the time and frequency domain indices, s is the input signal, w is the window function, and m is the window interval centered around zero. The STFT can also be interpreted as a uniform filter bank .The output signal X(n, k) is essentially the STFT (index n) obtained at the kth channel of the filter bank (Figure 1).The window function is assumed to be nonzero only in the.
Thus, nonstationary probability densities and statistical moments are obtained. Finally, two types of nonlinear structural models, that is, structural systems with memory and without memory, under different modulating ground motions are considered. scale and shape of the underlying processes, Koenker & Xiao () have introduced a particular subclass of random coe cient autoregressive models called quantile autoregressions. In this model, autoregressive coe cients are allowed to vary with the quantiles ˝2[0;1]. In contrast to many of.
Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 22) it is common practice to modulate a stationary stochastic process with a shaping window, or envelope function, in order to obtain a transient signal. Iyengar, K.T.S.: A nonstationary random process model for earthquake accelerograms. Bull. Sampling a Continuous-Time Process. Discrete 4. Linear Filters 5. Some Special Models 6. Some Spectral Theory for Nonstationary Processes 7. Nonlinear Transformations of Random Processes 8. Higher Order Spectra 9. Spectral Theory for GRPt Stationary Processes Time Processes Spectral Theories for Homogeneous Random Filters, General.
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Abstract. Real random processes are always nonstationary. However, there are many processes whose statistical parameters change sufficiently slowly with regard to time that one can make estimates of ‘moving spectra’ involving frequency and : W.
Gan. We propose a time-varying Wiener filter for nonstationary signal estimation that is robust in a minimax sense. This robust Wiener filter optimizes worst-case performance within novel “p-point” uncertainty classes of nonstationary random rmore, it features constant performance within these uncertainty classes and requires less detailed prior knowledge than the ordinary time Cited by: A commonly used regression scheme for nonstationary processes is known as ARIMA.
ARIMA models include an autoregressive component (AR), a moving-average (MA) component, and a differencing component.
In the model, these are the autoregressive parameters (p), the number of differencing passes (d), and the moving-average parameters (q). Adaptive identification problems on coefficient matrices of an autoregressive model for multivariate and one‐dimensional nonstationary Gaussian random processes are investigated by applying the extended Kalman filter incorporated with a weighted global by: A recursive algorithm for the model structure selection and for adaptive parameter estimation is presented.
An example of parametric spectral analysis of nonstationary autoregressive process is given. Keywords. Stochastic systems} time-varying systems} random processes} identification} parameter estimation} filtering} Kaiman : E.A.
Kliokys, A.A. Nemura. Many nonstationary random processes exhibit a "frequency modulated" structure. In this paper a method of modelling such processes as the output of a time variable filter driven by white noise is described.
The basis of the method relies on producing a process that is "covariance equivalent" to the process under consideration. Dynamic system with a random structure described by a set of the first-order stochastic differential equations (SDE) is used as a generating model of nonstationary pulse stochastic processes.
Harmonizable processes constitute an important class of nonstationary stochastic processes. In this paper, we present a theory of polyspectra (higher order moment spectra) for the harmonizable class. Synoptic winds are commonly schematized as a stationary random process, which can be decomposed as the sum of a constant mean wind velocity and three turbulence components (longitudinal, lateral and vertical), modelled as stationary Gaussian random processes.
In this Section, the model adopted for the psdf of the three turbulence components is. Mathematical models for these random phenomena are referred to as stochastic processes and/or random fields, and Monte Carlo simulation is the only general-purpose tool for solving problems of.
The nonstationary Wiener filter (WF) is the optimum linear system for estimating a nonstationary signal contaminated by nonstationary noise. nonstationary random processes. The energetic. Phase noise model Nonstationary phase noise.
When the system is frequency locked, the resulting phase noise is slowly varying but not limited, and it is modeled as a zero-mean, nonstationary, infinite-power Wiener process. In this case, the phase noise is expressed as a free-running or Brownian process.
and underspread nonstationary random processes (i.e., nonstationary processes that feature only small time-frequency correlations).
After brieﬂy describing the major diﬃculties encountered with time-varying systems and non-stationary processes, we introduce an extended deﬁnition of underspread systems. Our extended. This paper presents a novel time varying dynamic Bayesian network (TVDBN) model for the analysis of nonstationary sequences which are of interest in many fields.
The changing network structure and parameter in TVDBN are treated as random processes whose values at each time epoch determine a stationary DBN model; this DBN model is then used to. modeling of stationary random processes is well appreciated, a number of signals encountered in real life are nonstationary (e.g., speech signals).
This justifies the growing interest toward nonstationary signal analysis and TV-AR models, which arise naturally in speech analysis due to the changing shape of.
In the present paper, if the ambient excitation can be modeled as a nonstationary white noise in the form of a product model, then the nonstationary cross random decrement signatures of structural. The shaping filter, so named because it shapes a white noise input into a prescribed random process whose correlation function is known, was suggest- ed for use with the adjoint technique of analysis by Lanning and Battin (1),for stationary processes, and applied to nonstationary processes by Bailey, (2).
III Spectral Analysis of Random Processes by Model Fitting Spectral Analysis of Nonstationary Processes 12 Estimating Power Spectra using Criteria from Signal Compression using Shaping Filters Shaping filters for white signal sequences Sampling a Continuous‐Time Process.
Discrete time Processes. Linear Filters. Some Special Models. Some Spectral Theory for Nonstationary Processes. Nonlinear Transformations of Random Processes.
Higher Order Spectra. Spectral Theory for GRP. Spectral Theories for Homogeneous Random Processes on Other Spaces.
Filters, General Theory. Exercises. Nonstationary Stochastic Processes And Their Applications - Proceedings Of The Workshop. A Uniformly Modulated Nonstationary Model for Seismic Records; Estimation and Related Problems for Nonstationary Random Fields Generated by a Moving Source; An Overview of the Theory of Higher-Order Cyclostationarity.
Random Filters which Preserve. A timely update of the classic book on the theory and application of random data analysis First published inRandom Data served as an authoritative book on the analysis of experimental physical data for engineering and scientific applications.
This Fourth Edition features coverage of new developments in random data management and analysis procedures that are .White noise is the simplest example of a stationary process. An example of a discrete-time stationary process where the sample space is also discrete (so that the random variable may take one of N possible values) is a Bernoulli examples of a discrete-time stationary process with continuous sample space include some autoregressive and moving average processes which are .Characterization of cyclostationary random signal processes that fluctuate periodically with time.
In this paper we examine two methods for representing nonstationary processes that reveal the special properties possessed by CS processes. These examples demonstrate improvement in performance over that of filter designs based on.