scholarly journals Robust Autoregression with Exogenous Input Model for System Identification and Predicting

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 755
Author(s):  
Jiaxin Xie ◽  
Cunbo Li ◽  
Ning Li ◽  
Peiyang Li ◽  
Xurui Wang ◽  
...  
Keyword(s):  
System Identification ◽  
Model Fitting ◽  
Model Parameters ◽  
System Parameters ◽  
Arterial Blood ◽  
L2 Norm ◽  
Norm Space ◽  
Input Model ◽  
Dynamic Relationships ◽  
Electroencephalogram Eeg

Autoregression with exogenous input (ARX) is a widely used model to estimate the dynamic relationships between neurophysiological signals and other physiological parameters. Nevertheless, biological signals, such as electroencephalogram (EEG), arterial blood pressure (ABP), and intracranial pressure (ICP), are inevitably contaminated by unexpected artifacts, which may distort the parameter estimation due to the use of the L2 norm structure. In this paper, we defined the ARX in the Lp (p ≤ 1) norm space with the aim of resisting outlier influence and designed a feasible iteration procedure to estimate model parameters. A quantitative evaluation with various outlier conditions demonstrated that the proposed method could estimate ARX parameters more robustly than conventional methods. Testing with the resting-state EEG with ocular artifacts demonstrated that the proposed method could predict missing data with less influence from the artifacts. In addition, the results on ICP and ABP data further verified its efficiency for model fitting and system identification. The proposed Lp-ARX may help capture system parameters reliably with various input and output signals that are contaminated with artifacts.

Constructing and model-fitting receiver operator characteristics using continuous data.

2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor
Keyword(s):  
Signal Detection ◽  
Least Squares Method ◽  
Model Fitting ◽  
Simulated Data ◽  
Continuous Variable ◽  
Continuous Data ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Unequal Variance ◽  
Discrete Discrimination

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.

scholarly journals Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system

2021 ◽  
Author(s):  
Daniel Bittner ◽  
Beatrice Richieri ◽  
Gabriele Chiogna
Keyword(s):  
Groundwater Recharge ◽  
Temporal Variations ◽  
Hydrologic Model ◽  
Time Dependent ◽  
Model Parameters ◽  
Lumped Parameter ◽  
Hydrodynamic Processes ◽  
Input Model ◽  
Karst Model ◽  
Input Uncertainties

AbstractUncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.

scholarly journals Accounting for oxygen in the renal cortex: a computational study of factors that predispose the cortex to hypoxia

2017 ◽  
Vol 313 (2) ◽  
pp. F218-F236 ◽  
Author(s):  
Chang-Joon Lee ◽  
Bruce S. Gardiner ◽  
Jennifer P. Ngo ◽  
Saptarshi Kar ◽  
Roger G. Evans ◽  
...  
Keyword(s):  
Oxygen Tension ◽  
Renal Cortex ◽  
Computational Study ◽  
Ischemia Reperfusion ◽  
Sodium Reabsorption ◽  
Model Parameters ◽  
Arterial Blood ◽  
Increased Risk ◽  
Renal Oxygen Consumption ◽  
Renal Oxygen

We develop a pseudo-three-dimensional model of oxygen transport for the renal cortex of the rat, incorporating both the axial and radial geometry of the preglomerular circulation and quantitative information regarding the surface areas and transport from the vasculature and renal corpuscles. The computational model was validated by simulating four sets of published experimental studies of renal oxygenation in rats. Under the control conditions, the predicted cortical tissue oxygen tension ([Formula: see text]) or microvascular oxygen tension (µPo2) were within ±1 SE of the mean value observed experimentally. The predicted [Formula: see text] or µPo2 in response to ischemia-reperfusion injury, acute hemodilution, blockade of nitric oxide synthase, or uncoupling mitochondrial respiration, were within ±2 SE observed experimentally. We performed a sensitivity analysis of the key model parameters to assess their individual or combined impact on the predicted [Formula: see text] and µPo2. The model parameters analyzed were as follows: 1) the major determinants of renal oxygen delivery ([Formula: see text]) (arterial blood Po2, hemoglobin concentration, and renal blood flow); 2) the major determinants of renal oxygen consumption (V̇o2) [glomerular filtration rate (GFR) and the efficiency of oxygen utilization for sodium reabsorption (β)]; and 3) peritubular capillary surface area (PCSA). Reductions in PCSA by 50% were found to profoundly increase the sensitivity of [Formula: see text] and µPo2 to the major the determinants of [Formula: see text] and V̇o2. The increasing likelihood of hypoxia with decreasing PCSA provides a potential explanation for the increased risk of acute kidney injury in some experimental animals and for patients with chronic kidney disease.

Arterial pressure contour analysis for estimating human vascular properties

1976 ◽  
Vol 40 (2) ◽  
pp. 171-176 ◽  
Author(s):  
T. B. Watt ◽  
C. S. Burrus
Keyword(s):  
Cardiac Cycle ◽  
Vascular System ◽  
Model Parameters ◽  
Pressure Curve ◽  
Contour Analysis ◽  
Order Model ◽  
Third Order ◽  
Arterial Blood ◽  
Pressure Contour ◽  
Multiple Measurements

The form of an arterial blood pressure curve during the diastolic portion of the cardiac cycle was here employed to identify parameters in a third-order model of the vascular system. Calculated elastic and intertial characteristics of this fitted model then became clinically accessible indices of corresponding real vascular properties. This technique incurred no risk and little discomfort for the patient. Tested in theory, in animal experimentation, and in human observations, our procedure utilized a Gauss-Newton algorithm via digital computer to provide rapid model solutions from different starting values, from multiple measurements sites, and from normal or diseased patients. Model parameters thus determined defined ranges of normal variation and suggested a less compliant arterial bed in hypertensive than in normotensive patients.

System identification and robust position control for electro-hydraulic servo system using hybrid model predictive control

2017 ◽  
Vol 24 (18) ◽  
pp. 4145-4159 ◽  
Author(s):  
Hai-Bo Yuan ◽  
Hong-Cheol Na ◽  
Young-Bae Kim
Keyword(s):  
System Identification ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Position Control ◽  
Servo System ◽  
Model Parameters ◽  
Integral Control ◽  
Hybrid Controller ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

This paper examined system identification using grey-box model estimation and position-tracking control for an electro-hydraulic servo system (EHSS) using hybrid controller composed of proportional-integral control (PIC) and model predictive control (MPC). The nonlinear EHSS model is represented by differential equations. We identify model parameters and verify their accuracy against experimental data in MATLAB to evaluate the validity of this mathematical model. To guarantee improved performance of EHSS and precision of cylinder position, we propose a hybrid controller composed of PIC and MPC. The controller is designed using the Control Design and Simulation module in the Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW). A LabVIEW-based experimental rig is developed to apply the proposed controller in real time. Then, the validity and performance superiority of the hybrid controller were confirmed by comparing them with the MPC and PIC results. Results of real-life experiments show improved robustness and dynamic and static properties of EHSS.

scholarly journals Two-stage Bayesian system identification using Gaussian discrepancy model

2020 ◽  
pp. 147592172093352
Author(s):  
Feng-Liang Zhang ◽  
Siu-Kui Au ◽  
Yan-Chun Ni
Keyword(s):  
System Identification ◽  
Structural Model ◽  
Structural Parameters ◽  
Stage Ii ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Model Parameters ◽  
Modeling Error ◽  
Two Stage ◽  
Classical Statistics

System identification aims at updating the model parameters (e.g. mass and stiffness) associated with the mathematical model of a structure based on measured structural response. In this process, a two-stage approach is commonly adopted. In Stage I, modal parameters including natural frequencies and mode shapes are identified. In Stage II, the modal parameters are used to update structural parameters such as those related to stiffness, mass, and boundary conditions. A recent Bayesian formulation allows the identification results in the first stage to be incorporated in the second stage directly via Bayes’ rule without using a heuristic model (often based on classical statistics) that transfers the information from Stages I to II. This opens up opportunities for explicitly accounting for modeling error in the structural model (Stage II) through the conditional distribution of modal parameters given structural model parameters. Following this approach, this article investigates a methodology where the modeling error between the two stages is incorporated with Gaussian distributions whose statistical parameters are also updated with available data. Leveraging on special mathematical structure induced by the model, computational issues are resolved and an analytical investigation is performed that yields insights on the role of modeling error and whether its statistics can be distinguished from those of identification uncertainty (defined for given structural model). The proposed methodology is verified using synthetic data and applied to a laboratory-scale structure.

Fluid Tonicity Affects Peritoneal Characteristics Derived by 3-PORE Model

2019 ◽  
Vol 39 (3) ◽  
pp. 243-251 ◽  
Author(s):  
Joanna Stachowska-Pietka ◽  
Jan Poleszczuk ◽  
Josep Teixido-Planas ◽  
Josep Bonet-Sol ◽  
Maria I. Troya-Saborido ◽  
...  
Keyword(s):  
Model Fitting ◽  
Hydraulic Conductance ◽  
Membrane Properties ◽  
Patient Specific ◽  
Model Parameters ◽  
Peritoneal Membrane ◽  
Substantial Impact ◽  
Pore Model ◽  
Using Data ◽  
The Impact

Background It is typically assumed that within short time-frames, patient-specific peritoneal membrane characteristics are constant and do not depend on the initial fluid tonicity and dwell duration. The aim of this study was to check whether this assumption holds when membrane properties are estimated using the 3-pore model (3PM). Methods Thirty-two stable peritoneal dialysis (PD) patients underwent 3 8-hour peritoneal equilibration tests (PETs) with different glucose-based solutions (1.36%, 2.27%, and 3.86%). Temporary drainage was performed at 1 and 4 hours. Glucose, urea, creatinine, sodium, and phosphate concentrations were measured in dialysate and blood samples. Three-pore model parameters were estimated for each patient and each 8-hour PET separately. In addition, model parameters were estimated using data truncated to the initial 4 hours of peritoneal dwell. Results In all cases, model-estimated parameter values were within previously reported ranges. The peritoneal absorption (PA) and diffusive permeability for all solutes except sodium increased with fluid tonicity, with about 18% increase when switching from glucose 2.27% to 3.86%. Glucose peritoneal reflection coefficient and osmotic conductance (OsmCond), and fraction of hydraulic conductance for ultrasmall pores decreased with fluid tonicity (over 40% when switching from glucose 1.36%). Model fitting to the truncated 4-hour data resulted in little change in the parameters, except for PA, peritoneal hydraulic conductance, and OsmCond, for which higher values for the 4-hour dwell were found. Conclusion Initial fluid tonicity has a substantial impact on the 3PM-estimated characteristics of the peritoneal membrane, whereas the impact of dwell duration was relatively small and possibly influenced by the change in the patient's activity.

NON-PARAMETRIC IDENTIFICATION TECHNIQUES FOR INTELLIGENT PNEUMATIC ACTUATOR

2015 ◽  
Vol 77 (20) ◽  
Author(s):  
Abdulrahman A. A. Emhemed ◽  
Rosbi Mamat ◽  
Ahmad ‘Athif Mohd Faudzi
Keyword(s):  
System Identification ◽  
Parametric Identification ◽  
Pneumatic Actuator ◽  
Step Response ◽  
Model Parameters ◽  
Force Model ◽  
Force System ◽  
Identification Technique ◽  
Identification Techniques ◽  
Non Parametric

The aim of this paper is to present experimental, empirical and analytic identification techniques, known as non-parametric techniques. Poor dynamics and high nonlinearities are parts of the difficulties in the control of pneumatic actuator functions, which make the identification technique very challenging. Firstly, the step response experimental data is collected to obtain real-time force model of the intelligent pneumatic actuator (IPA). The IPA plant and Personal Computer (PC) communicate through Data Acquisition (DAQ) card over MATLAB software. The second method is approximating the process by curve reaction of a first-order plus delay process, and the third method uses the equivalent n order process with PTn model parameters. The obtained results have been compared with the previous study, achieved based on force system identification of IPA obtained by the (Auto-Regressive model with eXogenous) ARX model. The models developed using non-parameters identification techniques have good responses and their responses are close to the model identified using the ARX system identification model. The controller approved the success of the identification technique with good performance. This means the Non-Parametric techniques are strongly recommended, suitable, and feasible to use to analyze and design the force controller of IPA system. The techniques are thus very suitable to identify the real IPA plant and achieve widespread industrial acceptance.

Nondestructive Evaluation of Composite Structures Using System Identification Technique

1988 ◽  
Vol 110 (2) ◽  
pp. 134-139 ◽  
Author(s):  
W. H. Tsai ◽  
J. C. S. Yang
Keyword(s):  
System Identification ◽  
Nondestructive Test ◽  
Nondestructive Evaluation ◽  
Fatigue Damage ◽  
Composite Structures ◽  
Impact Damage ◽  
Epoxy Ring ◽  
System Parameters ◽  
Identification Technique

A system identification technique is presented for nondestructive test to detect and to characterize the existence and location of cracks and other damages in composite structures. Various composite structures, including Kevlar-epoxy plate, graphite epoxy ring, and graphite-epoxy coupon have been tested for different damages such as crack, delamination, impact damage, fatigue damage, etc. In addition, the correlation between severity of any type of the damages mentioned above and changes in identified system parameters has also been systematically studied.

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