Method for Determining System Eigenvalues From FRF for Noise Contaminated Subsystems

1993 ◽  
Author(s):  
H. C. Chen ◽  
Eric K. Lee ◽  
Y. G. Tsuei
Keyword(s):  
Frequency Response Function ◽  
Numerical Study ◽  
Moving Average ◽  
Arma Model ◽  
Frequency Range ◽  
Laplace Domain ◽  
Force Method ◽  
Modal Force ◽  
Auto Regressive ◽  
Inverse Operation

Abstract A method for determining the eigenvalues of a synthesized system from the Frequency Response Function (FRF) for noise contaminated subsystems is presented. This method first uses matrix Auto-Regressive Moving-Average (ARMA) model in the Laplace domain to describe each subsystem. Then a modal force method by ARMA model can be established. Only the FRF at the connecting joints is needed in the analysis to form a matrix named Modal Force Matrix. From this matrix, both synthesized system modes and substructure modes can be extracted simultaneously. Since the inverse operation is not required to form Modal Force Matrix, the computation is reduced drastically. The eigensolution of the system in any frequency range can be determined independently. Numerical study suggests that good results can be achieved by this method.

scholarly journals Forecasting and Modelling of Solar Radiation for Photovoltaic (PV) Systems

2021 ◽  
Author(s):  
Ines Sansa ◽  
Najiba Mrabet Bellaaj
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Cloud Cover ◽  
Moving Average ◽  
Arma Model ◽  
Weather Conditions ◽  
Pv Systems ◽  
Auto Regressive ◽  
Micro Grid ◽  
One Year

Solar radiation is characterized by its fluctuation because it depends to different factors such as the day hour, the speed wind, the cloud cover and some other weather conditions. Certainly, this fluctuation can affect the PV power production and then its integration on the electrical micro grid. An accurate forecasting of solar radiation is so important to avoid these problems. In this chapter, the solar radiation is treated as time series and it is predicted using the Auto Regressive and Moving Average (ARMA) model. Based on the solar radiation forecasting results, the photovoltaic (PV) power is then forecasted. The choice of ARMA model has been carried out in order to exploit its own strength. This model is characterized by its flexibility and its ability to extract the useful statistical properties, for time series predictions, it is among the most used models. In this work, ARMA model is used to forecast the solar radiation one year in advance considering the weekly radiation averages. Simulation results have proven the effectiveness of ARMA model to forecast the small solar radiation fluctuations.

ADAPTIVE, FREQUENCY DOMAIN, 2-D MODELING USING SPATIOTEMPORAL SIGNALS

1996 ◽  
Vol 06 (04) ◽  
pp. 351-358
Author(s):  
WASFY B. MIKHAEL ◽  
HAOPING YU
Keyword(s):  
Frequency Domain ◽  
Moving Average ◽  
System Modeling ◽  
Arma Model ◽  
Model Parameters ◽  
Two Dimensional ◽  
Convergence Factor ◽  
Arma Process ◽  
Auto Regressive ◽  
Unknown System

In this paper, an adaptive, frequency domain, steepest descent algorithm for two-dimensional (2-D) system modeling is presented. Based on the equation error model, the algorithm, which characterizes the 2-D spatially linear and invariant unknown system by a 2-D auto-regressive, moving-average (ARMA) process, is derived and implemented in the 3-D spatiotemporal domain. At each iteration, corresponding to a given pair of input and output 2-D signals, the algorithm is formulated to minimize the error-function’s energy in the frequency domain by adjusting the 2-D ARMA model parameters. A signal dependent, optimal convergence factor, referred to as the homogeneous convergence factor, is developed. It is the same for all the coefficients but is updated once per iteration. The resulting algorithm is called the Two-Dimensional, Frequency Domain, with Homogeneous µ*, Adaptive Algorithm (2D-FD-HAA). In addition, the algorithm is implemented using the 2-D Fast Fourier Transform (FFT) to enhance the computational efficiency. Computer simulations demonstrate the algorithm’s excellent adaptation accuracy and convergence speed. For illustration, the proposed algorithm is successfully applied to modeling a time varying 2-D system.

The Application of EMD and ARMA Bi-Cepstrum Fault Diagnosis Method in Gearbox of Overhead Traveling Crane

2011 ◽  
Vol 308-310 ◽  
pp. 88-91
Author(s):  
Hong Bo Xu ◽  
Guo Hua Chen ◽  
Xin Hua Wang ◽  
Jun Liang
Keyword(s):  
Fault Diagnosis ◽  
Evaluation Method ◽  
Moving Average ◽  
Arma Model ◽  
Time Varying ◽  
Stability Evaluation ◽  
Test Results ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Auto Regressive

For the time varying of signals, empirical mode decomposition (EMD) is occupied to modulate signals; auto-regressive moving average (ARMA) of higher accuracy is used to establish model for the signal principal components; then parametric bi-cepstrum estimation is implemented and fault feature is extracted. The test results about gearbox of overhead traveling crane indicate: the feature quefrency can be obtained through method of EMD and ARMA model parametric bi-cepstrum estimation.It is a kind of effective fault diagnosis and stability evaluation method.

scholarly journals ARMA Models to Measure the Scale of Fluctuation from CPT Data

2020 ◽  
Vol 14 (1) ◽  
pp. 230-236
Author(s):  
Brigid Cami ◽  
Sina Javankhoshdel
Keyword(s):  
Spatial Variability ◽  
Moving Average ◽  
Arma Model ◽  
The Other ◽  
True Value ◽  
A Value ◽  
First Case ◽  
Scale Of Fluctuation ◽  
Auto Regressive

Objective: Spatial variability is one of the largest sources of uncertainty in geotechnical applications. This variability is primarily characterized by the scale of fluctuation, a parameter that describes the distance over which the parameters of a material are similar. Spatial variability is generally described with traditional methods of time series analysis. In statistics, the Auto-Regressive Moving Average (ARMA) model is commonly used to describe the relationship between two points in time. Instead of assuming an autocorrelation model, the ARMA model calculates the necessary auto-regressive components (AR), as well as a decaying Mean Structure (MA). The advantage of this method is that it is calculated for each specific field study, so that the data is not forced to fit into a fixed autocorrelation model (e.g. Markovian, Gaussian, etc). Methods: In this study, the ARMA model is introduced as a means of measuring scale of fluctuation, and two case studies and a simulation are used to compare the scale of fluctuation values from the ARMA model to the other estimates. Results: In the first case study, the ARMA model estimated a value of 0.26 m while the other methods ranged from 0.22-0.29 m. In the second case study, the ARMA model estimated a value of 0.40 m while the other methods ranged from 0.40-0.54 m. In the simulated example, where the true value was 5.0 m, the ARMA model estimated a value of 4.73 m while the other methods ranged from 3.24-3.51 m. Conclusion: This paper concludes that ARMA is a promising new method for estimating the scale of fluctuation but requires a considerable amount of research before it can become established in the geotechnical sphere.

BLACK BOX MODELLING THE THERMAL BEHAVIOUR OF IHOUSE USING AUTO REGRESSIVE AND MOVING AVERAGE (ARMA) MODEL

2016 ◽  
Vol 78 (6-13) ◽  
Author(s):  
Shamsul Faisal Mohd Hussein ◽  
Hoaison Nguyen ◽  
Shahrum Shah Abdullah ◽  
Yuto Lim ◽  
Yasuo Tan
Keyword(s):  
Control System ◽  
Simulation Model ◽  
Thermal Behaviour ◽  
Moving Average ◽  
Arma Model ◽  
Black Box ◽  
Modelling Method ◽  
Auto Regressive ◽  
Recorded Data ◽  
Better Than

Modelling and simulation of the dynamic thermal behaviour of a building is important to test any proposed thermal comfort control system and strategy in the building. A simulation model can be obtained by using either the white box, grey box or black box modelling method. This research focuses on the usage of auto regressive and moving average (ARMA) model, a type of black box model that represents the dynamic thermal behaviour of iHouse testbed and uses real recorded data from the testbed and limited knowledge regarding the physical characteristics of the testbed. The performance of the ARMA model developed in this research is compared with the performance of House Thermal Simulator, a previously developed model, based on grey box modelling. Results obtained shows that ARMA model works better than House Thermal Simulator in some aspects.  

scholarly journals Solar Radiation Prediction Using a Novel Hybrid Model of ARMA and NARX

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6920
Author(s):  
Ines Sansa ◽  
Zina Boussaada ◽  
Najiba Mrabet Bellaaj
Keyword(s):  
Solar Radiation ◽  
Hybrid Model ◽  
Moving Average ◽  
Arma Model ◽  
Power Production ◽  
Auto Regressive ◽  
Efficient Prediction ◽  
Prediction Approach ◽  
A Company ◽  
Grid Management

The prediction of solar radiation has a significant role in several fields such as photovoltaic (PV) power production and micro grid management. The interest in solar radiation prediction is increasing nowadays so efficient prediction can greatly improve the performance of these different applications. This paper presents a novel solar radiation prediction approach which combines two models, the Auto Regressive Moving Average (ARMA) and the Nonlinear Auto Regressive with eXogenous input (NARX). This choice has been carried out in order to take the advantages of both models to produce better prediction results. The performance of the proposed hybrid model has been validated using a real database corresponding to a company located in Barcelona north. Simulation results have proven the effectiveness of this hybrid model to predict the weekly solar radiation averages. The ARMA model is suitable for small variations of solar radiation while the NARX model is appropriate for large solar radiation fluctuations.

Fuzzy Logic–Based Comparison of Predicted Sales Records of Competing Products

2021 ◽  
pp. 097226292097147
Author(s):  
Anuradha Banerjee
Keyword(s):  
Fuzzy Logic ◽  
Moving Average ◽  
Arma Model ◽  
Marketing Strategies ◽  
The Other ◽  
Anova Analysis ◽  
Future Prediction ◽  
Auto Regressive ◽  
Near Future ◽  
One Way Anova

The issue of comparing sales records of competitors is gaining increased importance to both marketing academicians and practitioners to get an idea about approximate trend of customer inclination to their products. Actual sales records of competing products for past few years can be compared in two ways. If sales records exhibit normal distribution, then they can be tested for dominance over the other using t test (paired or unpaired). On the other hand, if normality is violated, then non-parametric tests like Kruskal–Wallis test by ranks or one-way ANOVA (analysis of variance) can be applied to test whether samples originate from the same distribution. One-way ANOVA is very flexible in the sense that it can work with two or more independent samples, and sample sizes need not be equal. This article emphasizes the fact that marketing strategies of today must take care of predicted consumer inclination, at least in the near future. Prediction of future sales records of competing products can be obtained using many techniques available in the literature, like linear regression, auto-regressive moving average (ARMA) model etc. All these predictions come up with a certain percentage of error. Therefore, it is wise to fuzzify them by dividing into ranges, before comparison. Here, a novel fuzzy logic–based technique is proposed that compares predicted sales records of competing products and accordingly finds out which one is the best.

Prediction of Network Utilization Based on ARMA Model and RELS

2014 ◽  
Vol 945-949 ◽  
pp. 2780-2783 ◽  
Author(s):  
Hui Zhang ◽  
Fang He ◽  
Chun Yan Han
Keyword(s):  
Moving Average ◽  
Arma Model ◽  
Estimation Algorithm ◽  
Networked Control System ◽  
Model Parameters ◽  
Predictive Algorithm ◽  
Network Utilization ◽  
Parameter Estimation Algorithm ◽  
General Network ◽  
Auto Regressive

This paper focused on predictive algorithm of network utilization for networked control system (NCS). Auto-Regressive and Moving Average (ARMA) model was presented for general network utilization, which with fixed constant and known white noise. ARMA model parameters are estimated using parameter estimation algorithm of Recursive Extended Least Squares (RELS). Finally, a simulation example was given to realize RELS of ARMA model. Predictive output of network utilization can be obtained and converge to real state.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

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