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.

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.

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.

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.

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.

Nonlinear Time-Series Prediction Using a Single MEMS Reservoir

2020 ◽  
Author(s):  
Mohammad H. Hasan ◽  
Fadi Alsaleem
Keyword(s):  
Moving Average ◽  
System Modeling ◽  
Time Series Prediction ◽  
Mems Devices ◽  
Temporal Dependence ◽  
Dynamical System Modeling ◽  
Random Modulation ◽  
Auto Regressive ◽  
Mems Device ◽  
Computing Scheme

Abstract In this work, we show the computational potential of MEMS devices by predicting the dynamics of a 10th order nonlinear auto-regressive moving average (NARMA10) dynamical system. Modeling this system is considered complex due to its high nonlinearity and dependency on its previous values. To model the NARMA10 system, we used a reservoir computing scheme by utilizing one MEMS device as a reservoir, produced by the interaction of 100 virtual nodes. The virtual nodes are attained by sampling the input of the MEMS device and modulating this input using a random modulation mask. The interaction between virtual nodes within the system was produced through delayed feedback and temporal dependence. Using this approach, the MEMS device was capable of adequately capturing the NARMA10 response with a normalized root mean square error (NRMSE) = 6.18% and 6.43% for the training and testing sets, respectively. In practice, the MEMS device would be superior to simulated reservoirs due to its ability to perform this complex computing task in real time.

Properties of the spatial unilateral first-order ARMA model

1993 ◽  
Vol 25 (03) ◽  
pp. 631-648 ◽  
Author(s):  
Sabyasachi Basu ◽  
Gregory C. Reinsel
Keyword(s):  
Spatial Data ◽  
Likelihood Function ◽  
Moving Average ◽  
Spatial Models ◽  
Arma Model ◽  
Substantial Effect ◽  
Two Dimensional ◽  
Estimation Of Parameters ◽  
First Order ◽  
Spatial Lattice Data

For two-dimensional spatial data, a spatial unilateral autoregressive moving average (ARMA) model of first order is defined and its properties studied. The spatial correlation properties for these models are explicitly obtained, as well as simple conditions for stationarity and conditional expectation (interpolation) properties of the model. The multiplicative or linear-by-linear first-order spatial models are seen to be a special case which have proved to be of practical use in modeling of two-dimensional spatial lattice data, and hence the more general models should prove to be useful in applications. These unilateral models possess a convenient computational form for the exact likelihood function, which gives proper treatment to the border cell values in the lattice that have a substantial effect in estimation of parameters. Some simulation results to examine properties of the maximum likelihood estimator and a numerical example to illustrate the methods are briefly presented.

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