scholarly journals The Autoregressive Moving Average with Exogenous Excitation Model for Acoustic Scattering from Underwater Objects

2020 ◽  
Vol 18 (02) ◽  
pp. 157-162
Author(s):  
Lubna Farhi ◽  
Farhan Ur Rehman ◽  
Agha Yasir Ali
Keyword(s):  
Time Domain ◽  
Moving Average ◽  
Acoustic Scattering ◽  
Autoregressive Moving Average ◽  
Mean Square ◽  
Exogenous Variables ◽  
Scattering Response ◽  
Mean Square Error Criterion ◽  
Excitation Model ◽  
Initial Results

This study aims to identify and predict objects underwater using the autoregressive moving average with exogenous excitation (ARMX) model in such a way that the outcome of the model is similar to actual measurements. It is used for parameter estimation. This model is validated by comparing results in actual model with ARMX model, autoregressive with an exogenous variables, and Box Jenkins (BJ) model. The results are analyzed in frequency and time domain by using mean square error criterion. Initial results show that ARMX predicts the acoustic scattering response with an accuracy of 96%, while ARX provides an accuracy of 78%, and BJ model poorly estimates the signal with an accuracy of 35%. ARMX also provides higher accuracy of detection by 7-8% as compared to the existing techniques.

Time-Domain Filtering of Two-Dimensional Fluorescence Data

1986 ◽  
Vol 40 (4) ◽  
pp. 542-548 ◽  
Author(s):  
Maria Vicsek ◽  
Sharon L. Neal ◽  
Isiah M. Warner
Keyword(s):  
Error Analysis ◽  
Mean Square Error ◽  
Time Domain ◽  
Moving Average ◽  
Mean Square ◽  
Two Dimensional ◽  
Spline Method ◽  
Dimensional Version ◽  
The Difference ◽  
Polynomial Smoothing

Four time-domain filtering methods are applied to simulated and experimental two-dimensional fluorescence data in order to evaluate their performance. The methods that were evaluated are (1) moving average, (2) Savitsky-Golay polynomial smoothing, (3) Chebyshev filtering, and (4) bicubic spline filtering. The methods are compared with the use of mean square error analysis and the difference in the amplitudes of the filtered noisy and ideal data. The two-dimensional version of the Savitzky-Golay filtering and the spline method produced the best overall results.

Respiratory sinus arrhythmia: time domain characterization using autoregressive moving average analysis

1995 ◽  
Vol 268 (6) ◽  
pp. H2232-H2238 ◽  
Author(s):  
J. K. Triedman ◽  
M. H. Perrott ◽  
R. J. Cohen ◽  
J. P. Saul
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Time Domain ◽  
Rate Control ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Heart Rate Control ◽  
Sinus Arrhythmia ◽  
Arterial Blood ◽  
Autonomic Blockade

Fourier-based techniques are mathematically noncausal and are therefore limited in their application to feedback-containing systems, such as the cardiovascular system. In this study, a mathematically causal time domain technique, autoregressive moving average (ARMA) analysis, was used to parameterize the relations of respiration and arterial blood pressure to heart rate in eight humans before and during total cardiac autonomic blockade. Impulse-response curves thus generated showed the relation of respiration to heart rate to be characterized by an immediate increase in heart rate of 9.1 +/- 1.8 beats.min-1.l-1, followed by a transient mild decrease in heart rate to -1.2 +/- 0.5 beats.min-1.l-1 below baseline. The relation of blood pressure to heart rate was characterized by a slower decrease in heart rate of -0.5 +/- 0.1 beats.min-1.mmHg-1, followed by a gradual return to baseline. Both of these relations nearly disappeared after autonomic blockade, indicating autonomic mediation. Maximum values obtained from the respiration to heart rate impulse responses were also well correlated with frequency domain measures of high-frequency "vagal" heart rate control (r = 0.88). ARMA analysis may be useful as a time domain representation of autonomic heart rate control for cardiovascular modeling.

Genetic algorithm for dynamic parameters estimation of the machine tool worktable using the residual vibration signal

2021 ◽  
pp. 107754632199357
Author(s):  
Xiaodong Wang ◽  
Bin Liu ◽  
Xuesong Mei ◽  
Xialun Yun ◽  
Xiao Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Time Domain ◽  
Average Method ◽  
Moving Average ◽  
Vibration Signal ◽  
Autoregressive Moving Average ◽  
Dynamic Parameters ◽  
Residual Vibration ◽  
Time Domain Signal ◽  
The Time Domain

In this article, the residual vibration signal of the worktable of the machine tool is used to identify its dynamic parameters. By analyzing the time-domain characteristics of the residual vibration signal, it is found that the signal has the similar attenuation characteristics to the impulse response of the second-order underdamped system, so a new method for identifying the dynamic parameters of the worktable is proposed. In this method, the impulse response model of the second-order underdamped system is used to describe the residual vibration signal, so the parameter identification problem based on the time-domain signal is transformed into the optimization problem of the model parameters. Then, the dynamic parameters can be determined by using the genetic algorithm. The results of the genetic algorithm are compared with the results of the autoregressive moving average method, which is a commonly used parameter identification method based on the time-domain signal. It was found that the results of the model-based method proposed in this article are more accurate than the autoregressive moving average method.

Performance analysis of global local mean square error criterion of stochastic linearization for nonlinear oscillator

2019 ◽  
Author(s):  
Nguyen Cao Thang ◽  
Luu Xuan Hung
Keyword(s):  
Performance Analysis ◽  
Mean Square Error ◽  
Nonlinear Oscillators ◽  
Degree Of Freedom ◽  
Equivalent Linearization ◽  
Mean Square ◽  
Error Criterion ◽  
Stochastic Linearization ◽  
Local Mean ◽  
Mean Square Error Criterion

The paper presents a performance analysis of global-local mean square error criterion of stochastic linearization for some nonlinear oscillators. This criterion of stochastic linearization for nonlinear oscillators bases on dual conception to the local mean square error criterion (LOMSEC). The algorithm is generally built to multi degree of freedom (MDOF) nonlinear oscillators. Then, the performance analysis is carried out for two applications which comprise a rolling ship oscillation and two degree of freedom one. The improvement on accuracy of the proposed criterion has been shown in comparison with the conventional Gaussian equivalent linearization (GEL).

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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