Linear algebraic approaches for (almost) periodic moving average system identification

2002 ◽  
Author(s):  
Ying-Chang Liang ◽  
A.R. Leyman ◽  
Xian-Da Zhang
Keyword(s):  
System Identification ◽  
Moving Average ◽  
Almost Periodic ◽  
Average System ◽  
Algebraic Approaches

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.

System identification models for waves in the inner magnetosphere

2020 ◽  
Author(s):  
Richard Boynton ◽  
Homayon Aryan ◽  
Walker Simon ◽  
Michael Balikhin
Keyword(s):  
Machine Learning ◽  
Solar Wind ◽  
System Identification ◽  
Moving Average ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Time Lags ◽  
Inner Magnetosphere ◽  
Geomagnetic Indices ◽  
The Waves

<p>This research develops forecast models of the spatiotemporal evolution of emissions throughout the inner magnetosphere between L=2-6 and at all MLT. The system identification, or machine learning, technique based on Nonlinear AutoRegressive Moving Average eXogenous (NARMAX) models is employed to deduce the forecasting models of the lower band chorus, Hiss, and magnetosonic waves using solar wind and geomagnetic indices as inputs. It is difficult to develop machine leaning based spatiotemporal models of the waves in the inner magnetosphere as the data is sparse and machine learning techniques require large amounts of data to deduce a model. To solve this problem, the spatial co-ordinates at the time of the measurements are included as inputs to the model along with time lags of the solar wind and geomagnetic indices, while the measurement of the waves by the Van Allen Probes are used as the output to train the models. The estimates of the resultant models are compared with separate data to the training data to assess the performance of the models. The models are then used to reconstruct the spatiotemporal waves over the inner magnetosphere, as the waves respond to changes in the solar wind and geomagnetic indices.  </p>

scholarly journals Modelling COVID-19 Pandemic Dynamics Using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) Machine Learning Models: A Case Study from Systems Thinking and System Identification Perspectives

2021 ◽  
Author(s):  
Hua-Liang Wei ◽  
Stephen A Billings
Keyword(s):  
Machine Learning ◽  
System Identification ◽  
Systems Thinking ◽  
Systems Engineering ◽  
Moving Average ◽  
A Priori ◽  
Learning Models ◽  
New Findings ◽  
Machine Learning Models

Since the outbreak of COVID-19, an astronomical number of publications on the pandemic dynamics appeared in the literature, of which many use the susceptible infected removed (SIR) and susceptible exposed infected removed (SEIR) models, or their variants, to simulate and study the spread of the coronavirus. SIR and SEIR are continuous-time models which are a class of initial value problems (IVPs) of ordinary differential equations (ODEs). Discrete-time models such as regression and machine learning have also been applied to analyze COVID-19 pandemic data (e.g. predicting infection cases), but most of these methods use simplified models involving a small number of input variables pre-selected based on a priori knowledge, or use very complicated models (e.g. deep learning), purely focusing on certain prediction purposes and paying little attention to the model interpretability. There have been relatively fewer studies focusing on the investigations of the inherent time-lagged or time-delayed relationships e.g. between the reproduction number (R number), infection cases, and deaths, analyzing the pandemic spread from a systems thinking and dynamic perspective. The present study, for the first time, proposes using systems engineering and system identification approach to build transparent, interpretable, parsimonious and simulatable (TIPS) dynamic machine learning models, establishing links between the R number, the infection cases and deaths caused by COVID-19. The TIPS models are developed based on the well-known NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs) model, which can help better understand the COVID-19 pandemic dynamics. A case study on the UK COVID-19 data is carried out, and new findings are detailed. The proposed method and the associated new findings are useful for better understanding the spread dynamics of the COVID-19 pandemic.

scholarly journals EXPERIMENTAL IDENTIFICATION OF AN ELASTO-MECHANICAL MULTI-DEGREE-OF-FREEDOM-SYSTEM USING STOCHASTIC SIGNALS

2016 ◽  
Vol 7 ◽  
pp. 64 ◽  
Author(s):  
Simon Schleiter ◽  
Okyay Altay ◽  
Sven Klinkel
Keyword(s):  
System Identification ◽  
Civil Engineering ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Engineering Structures ◽  
Stochastic Signals ◽  
Identification Methods ◽  
Recent Developments ◽  
Rc Building ◽  
Frequency Domain Decomposition

The determination of dynamic parameters are the central points of the system identification of civil engineering structures under dynamic loading. This paper first gives a brief summary of the recent developments of the system identification methods in civil engineering and describes mathematical models, which enable the identification of the necessary parameters using only stochastic input signals. Relevant methods for this identification use Frequency Domain Decomposition (FDD), Autoregressive Moving Average Models (ARMA) and the Autoregressive Models with eXogenous input (ARX). In a first step an elasto-mechanical mdof-system is numerically modeled using FEM and afterwards tested numerically by above mentioned identification methods using stochastic signals. During the second campaign, dynamic measurements are conducted experimentally on a real 7-story RC-building with ambient signal input using sensors. The results are successfully for the relevant system identification methods.

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