(Almost) periodic moving average system identification using higher order cyclic-statistics

1998 ◽  
Vol 46 (3) ◽  
pp. 779-783 ◽  
Author(s):  
Ying-Chang Liang ◽  
A.R. Leyman
Keyword(s):  
System Identification ◽  
Moving Average ◽  
Higher Order ◽  
Almost Periodic ◽  
Average System ◽  
Cyclic Statistics

(Almost) periodic FIR system identification using third-order cyclic-statistics

1997 ◽  
Vol 33 (5) ◽  
pp. 356 ◽  
Author(s):  
Ying-Chang Liang ◽  
A.R. Leyman ◽  
Boon-Hee Soong
Keyword(s):  
System Identification ◽  
Almost Periodic ◽  
Third Order ◽  
Cyclic Statistics ◽  
Fir System

scholarly journals PEMODELAN JUMLAH TINDAK KRIMINALITAS DI PROVINSI JAWA TIMUR DENGAN ANALISIS REGRESI SPATIAL AUTOREGRESSIVE AND MOVING AVERAGE

2018 ◽  
Vol 7 (4) ◽  
pp. 346
Author(s):  
NI MADE LASTI LISPANI ◽  
I WAYAN SUMARJAYA ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Population Density ◽  
Regression Model ◽  
Average Length ◽  
Moving Average ◽  
Spatial Regression ◽  
Higher Order ◽  
Spatial Effect ◽  
Spatial Regression Model ◽  
Spatial Autoregressive ◽  
Total Average

One of spatial regression model is spatial autoregressive and moving average (SARMA) which assumes that there is a spatial effect on dependent variable and error. SARMA can analyze the spatial effect on the higher order. The purpose of this research is to estimate the model of the total crime in East Java along with factors that affect it. The results show that the model can describe total crime in East Java is SARMA(0,1). The factors that influence the total crime  are population density (), poverty total (), average length of education at every regency/city and error from the neigbors.

Advanced System Identification of Plates Using a Higher-Order-Spectral Approach: Theory and Experiment

2011 ◽  
Author(s):  
Michele Pasquali ◽  
Walter Lacarbonara ◽  
Pier Marzocca
Keyword(s):  
System Identification ◽  
Dynamic Response ◽  
Experimental Testing ◽  
Nonlinear System Identification ◽  
Higher Order ◽  
Detection Mechanism ◽  
Specific Objective ◽  
Structural Nonlinearities ◽  
Identification Technique ◽  
Approach Theory

A nonlinear system identification technique exploiting the dynamic response features of fully nonlinear physics-based plate models extracted by Higher-Order Spectral (HOS) analysis tools is developed. The changes induced by an imperfection in the dynamics through the structural nonlinearities are used as key detection mechanism. The differences in dynamic response of a baseline and a modified/imperfect structure are enhanced by the local nonlinearities induced by the structural modification which thus represent the specific objective of identification. The validation of the procedure and the developed algorithms is carried out through extensive experimental testing employing various plates, including isotropic and composite lay-ups, and excitation sources, including White Gaussian Noise and a train of impulses.

Closed-loop parametric identification of DC-DC converter

2018 ◽  
Vol 232 (10) ◽  
pp. 1429-1438 ◽  
Author(s):  
Subhransu Padhee ◽  
Umesh Chandra Pati ◽  
Kamalakanta Mahapatra
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Model Simulation ◽  
Moving Average ◽  
Parametric Identification ◽  
Buck Converter ◽  
Input Output ◽  
Closed Loop System ◽  
Auto Regressive ◽  
Error Method

This study provides a step-by-step analysis of closed-loop parametric system identification for DC-DC buck converter. In closed-loop parametric identification, input–output experimental data are used to estimate the transfer function coefficients of DC-DC buck converter. For system identification purpose, a high-frequency perturbation signal is injected in to the closed-loop system which acts as an input signal for identification experiment. Different input–output models such as Auto-Regressive eXogenous, Auto-Regressive Moving Average with eXogenous, output error, and Box–Jenkins are used to model the converter structure and prediction error method is used to estimate the parameters. Model validation schemes are used to validate the estimated model. Simulation and experimental analysis have been provided to validate the results obtained.

Autoregressive logistic processes

1989 ◽  
Vol 26 (03) ◽  
pp. 524-531 ◽  
Author(s):  
Barry C. Arnold ◽  
C. A. Robertson
Keyword(s):  
Markov Process ◽  
Stochastic Model ◽  
Moving Average ◽  
Higher Order ◽  
Invariant Distribution ◽  
Absolutely Continuous ◽  
Joint Distributions ◽  
Moving Average Processes ◽  
Stationary Markov Process

A stochastic model is presented which yields a stationary Markov process whose invariant distribution is logistic. The model is autoregressive in character and is closely related to the autoregressive Pareto processes introduced earlier by Yeh et al. (1988). The model may be constructed to have absolutely continuous joint distributions. Analogous higher-order autoregressive and moving average processes may be constructed.

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