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.

Predictive Algorithm of Network Delay Based on Robust Kalman Filter

2014 ◽  
Vol 631-632 ◽  
pp. 937-940
Author(s):  
Fang He ◽  
Hui Zhang ◽  
Bing Bing Li
Keyword(s):  
Network Performance ◽  
Moving Average ◽  
Arma Model ◽  
Estimation Algorithm ◽  
Networked Control System ◽  
System Stability ◽  
Network Delay ◽  
Predictive Algorithm ◽  
Parameter Estimation Algorithm ◽  
Set Up

Due to irregular information flow for NCS (Networked Control System), network delay has performance of random and variability. It reduces system stability, network performance and control performance. This paper focuses on research of predictive algorithm of network delay. Network delay data is obtained in PROFIBUS-DP. Based on network delay data, the ARMA (Auto-Regressive and Moving Average) model of delay is set up. The parameter estimation algorithm of Robust Kalman is used to estimate parameters of proposed ARMA model of network delay. A simulation example is given and verifies efficiency of predictive algorithm proposed.

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.

“Sintering” Models and In-Situ Experiments: Data Assimilation for Microstructure Prediction in SLS Additive Manufacturing of Nylon Components

MRS Advances ◽  
2020 ◽  
Vol 5 (29-30) ◽  
pp. 1593-1601
Author(s):  
W. Steven Rosenthal ◽  
Francesca C. Grogan ◽  
Yulan Li ◽  
Erin I. Barker ◽  
Josef F. Christ ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Selective Laser Sintering ◽  
Phase Field Model ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Multiple Data ◽  
In Situ Experiments ◽  
Parameter Estimation Algorithm ◽  
Microstructure Prediction

ABSTRACTSelective laser sintering methods are workhorses for additively manufacturing polymer-based components. The ease of rapid prototyping also means it is easy to produce illicit components. It is necessary to have a data-calibrated in-situ physical model of the build process in order to predict expected and defective microstructure characteristics that inform component provenance. Toward this end, sintering models are calibrated and characteristics such as component defects are explored. This is accomplished by assimilating multiple data streams, imaging analysis, and computational model predictions in an adaptive Bayesian parameter estimation algorithm. From these data sources, along with a phase-field model, bulk porosity distributions are inferred. Model parameters are constrained to physically-relevant search directions by sensitivity analysis, and then matched to predictions using adaptive sampling. Using this feedback loop, data-constrained estimates of sintering model parameters along with uncertainty bounds are obtained.

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.

scholarly journals Least-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Weili Xiong ◽  
Wei Fan ◽  
Rui Ding
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Least Squares ◽  
Moving Average ◽  
Estimation Algorithm ◽  
Autoregressive Moving Average ◽  
Parameter Estimates ◽  
Persistent Excitation ◽  
Estimation Algorithms ◽  
Parameter Estimation Algorithm

This paper studies least-squares parameter estimation algorithms for input nonlinear systems, including the input nonlinear controlled autoregressive (IN-CAR) model and the input nonlinear controlled autoregressive autoregressive moving average (IN-CARARMA) model. The basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algorithm to estimate the unknown parameter vectors. It is proved that the parameter estimates consistently converge to their true values under the persistent excitation condition. A simulation example is provided.

Decomposition based least squares iterative estimation algorithm for output error moving average systems

2014 ◽  
Vol 31 (4) ◽  
pp. 709-725 ◽  
Author(s):  
Wenge Zhang
Keyword(s):  
Least Squares ◽  
Design Methodology ◽  
Moving Average ◽  
Estimation Algorithm ◽  
Identification Algorithm ◽  
Iterative Identification ◽  
Iterative Estimation ◽  
Content Type ◽  
Engineering Computation ◽  
Parameter Estimation Algorithm

Purpose – The purpose of this paper is to solve the heavy computational problem of parameter estimation algorithm. Design/methodology/approach – Presents a decomposition least squares based iterative identification algorithm. Findings – Can estimate the parameters for linear or pseudo-linear systems and have lower computational burden. Originality/value – This paper adopts a decomposition technique to solve engineering computation problems and offers a potential and efficient algorithm.

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