Identification of Linear Model Parameters and Uncertainties for an Aircraft Turbofan Engine

1997 ◽  
Vol 20 (6) ◽  
pp. 1274-1276 ◽  
Author(s):  
Roman Leibov
Keyword(s):  
Linear Model ◽  
Model Parameters ◽  
Turbofan Engine

scholarly journals Least Squares Estimations for the General Linear Model Parameters with Epsilon Skew Normal Error Term

2020 ◽  
pp. 636-645
Author(s):  
Hussain Karim Nashoor ◽  
Ebtisam Karim Abdulah
Keyword(s):  
Least Squares ◽  
Linear Model ◽  
Least Squares Method ◽  
General Linear Model ◽  
Ordinary Least Squares ◽  
Skewed Distribution ◽  
General Linear ◽  
Average Error ◽  
Model Parameters ◽  
Skew Normal

Examination of skewness makes academics more aware of the importance of accurate statistical analysis. Undoubtedly, most phenomena contain a certain percentage of skewness which resulted to the appearance of what is -called "asymmetry" and, consequently, the importance of the skew normal family . The epsilon skew normal distribution ESN (μ, σ, ε) is one of the probability distributions which provide a more flexible model because the skewness parameter provides the possibility to fluctuate from normal to skewed distribution. Theoretically, the estimation of linear regression model parameters, with an average error value that is not zero, is considered a major challenge due to having difficulties, as no explicit formula to calculate these estimates can be obtained. Practically, values for these estimates can be obtained only by referring to numerical methods. This research paper is dedicated to estimate parameters of the Epsilon Skew Normal General Linear Model (ESNGLM) using an adaptive least squares method, as along with the employment of the ordinary least squares method for estimating parameters of the General Linear Model (GLM). In addition, the coefficient of determination was used as a criterion to compare the models’ preference. These methods were applied to real data represented by dollar exchange rates. The Matlab software was applied in this work and the results showed that the ESNGLM represents a satisfactory model. 

scholarly journals Generalized Exponentiated Weibull Linear Model in the Presence of Covariates

2017 ◽  
Vol 6 (3) ◽  
pp. 75
Author(s):  
Tiago V. F. Santana ◽  
Edwin M. M. Ortega ◽  
Gauss M. Cordeiro ◽  
Adriano K. Suzuki
Keyword(s):  
Regression Model ◽  
Linear Model ◽  
Goodness Of Fit ◽  
Likelihood Function ◽  
Confidence Bands ◽  
Model Parameters ◽  
Random Component ◽  
Monte Carlo Simulation Study ◽  
New Model ◽  
Exponentiated Weibull

A new regression model based on the exponentiated Weibull with the structure distribution and the structure of the generalized linear model, called the generalized exponentiated Weibull linear model (GEWLM), is proposed. The GEWLM is composed by three important structural parts: the random component, characterized by the distribution of the response variable; the systematic component, which includes the explanatory variables in the model by means of a linear structure; and a link function, which connects the systematic and random parts of the model. Explicit expressions for the logarithm of the likelihood function, score vector and observed and expected information matrices are presented. The method of maximum likelihood and a Bayesian procedure are adopted for estimating the model parameters. To detect influential observations in the new model, we use diagnostic measures based on the local influence and Bayesian case influence diagnostics. Also, we show that the estimates of the GEWLM are  robust to deal with the presence of outliers in the data. Additionally, to check whether the model supports its assumptions, to detect atypical observations and to verify the goodness-of-fit of the regression model, we define residuals based on the quantile function, and perform a Monte Carlo simulation study to construct confidence bands from the generated envelopes. We apply the new model to a dataset from the insurance area.

scholarly journals Generalized Born radii computation using linear models and neural networks

2019 ◽  
Vol 36 (6) ◽  
pp. 1757-1764
Author(s):  
Saida Saad Mohamed Mahmoud ◽  
Gennaro Esposito ◽  
Giuseppe Serra ◽  
Federico Fogolari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Model ◽  
Correlation Coefficient ◽  
Linear Models ◽  
Reference Method ◽  
Supplementary Information ◽  
Model Parameters ◽  
Generalized Born ◽  
Supplementary Material

Abstract Motivation Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by algorithms based on the electrostatics of inhomogeneous dielectric media. The speed and accuracy of such computations are still an issue especially for their intensive use in classical molecular dynamics. Here, we propose an alternative approach that encodes the physics of the phenomena and the chemical structure of the molecules in model parameters which are learned from examples. Results GB radii have been computed using (i) a linear model and (ii) a neural network. The input is the element, the histogram of counts of neighbouring atoms, divided by atom element, within 16 Å. Linear models are ca. 8 times faster than the most widely used reference method and the accuracy is higher with correlation coefficient with the inverse of ‘perfect’ GB radii of 0.94 versus 0.80 of the reference method. Neural networks further improve the accuracy of the predictions with correlation coefficient with ‘perfect’ GB radii of 0.97 and ca. 20% smaller root mean square error. Availability and implementation We provide a C program implementing the computation using the linear model, including the coefficients appropriate for the set of Bondi radii, as Supplementary Material. We also provide a Python implementation of the neural network model with parameter and example files in the Supplementary Material as well. Supplementary information Supplementary data are available at Bioinformatics online.

Experimental Non-Linear Model Updating Applied in Cylindrical Journal Bearings

2001 ◽  
Author(s):  
Katia Lucchesi Cavalca ◽  
Sérgio Junichi Idehara ◽  
Franco Giuseppe Dedini ◽  
Robson Pederiva
Keyword(s):  
Linear Model ◽  
Model Updating ◽  
Optimization Method ◽  
Model Parameters ◽  
Numeric Model ◽  
Rotating Systems ◽  
Non Linear ◽  
Symmetric Rotor ◽  
Set Up ◽  
Experimental Values

Abstract The present paper proposes the use of non linear model updating applying unrestricted optimization method, in order to obtain a methodology, which allows the calibration of mathematical models in rotating systems. An experimental set up for this purpose consists of a symmetric rotor, on a rigid foundation supported by two hidrodynamic cylindrical bearings and with a central disk of considerable mass, working as na unbalancing excitation force. Once the numeric and experimental values are obtained, error vectors are defined, which are the minimization parameters, through the variation of the numeric model parameters. The method presented satisfactory results, as it was able to calibrate the mathematical model, and then to obtain reliable responses for the physical system studied. The research also presents a contribution for the rotating machine desing area as it presents a relatively simple methodology on the updating and revalidation of computacional models for machines and structures.

DefVolc: Interface and web service for fast computation of volcano displacement

2020 ◽  
Author(s):  
Valerie Cayol ◽  
Farshid Dabaghi ◽  
Yo Fukushima ◽  
Marine Tridon ◽  
Delphine Smittarello ◽  
...  
Keyword(s):  
Web Service ◽  
Linear Model ◽  
Web Server ◽  
Model Parameters ◽  
Magma Reservoirs ◽  
Surface Displacements ◽  
Stress Changes ◽  
Speed Up ◽  
Source Geometry ◽  
Volcano Observatories

<div> <div> <div> <p>DefVolc is a suite of programs and a web service intended to help the rapid interpretation of InSAR data, acquired on volcanoes at an increased frequency thanks to the various dedicated satellites. Our objective is to help to rapidely inverse volcano displacements, whether these displacements result from fractures (sheet intrusions or faults) or massive magma reservoirs. These sources may have complex geometries, and they may deform simultaneously. Moreover, volcanoes are associated with prominent topographies. This makes the analysis of surface displacements complex. To appropriately analyse the InSAR displacements, we conduct inverse modelling, using 3D elastostatic boundary element models and a neighbourhood optimization algorithm . We simultaneously invert non-linear model parameters (source geometry and location) and linear model parameters (source stress changes), and further assess mean model parameters and confidence intervals. In order to speed up the setting up of inversions, we developed a users friendly graphical interface. In order to accelerate the inversions, they run on clusters. A web server is proposed to registered users in order to run the inversions on University Clermont Auvergne clusters. Because the web server was developped in the framework of the Eurovolc project framework, European volcano observatories are priority users.</p> </div> </div> </div>

scholarly journals Urban development trend analysis and spatial simulation based on time series remote sensing data: A case study of Jinan, China

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257776
Author(s):  
Yanghua Zhang ◽  
Liang Zhao ◽  
Hu Zhao ◽  
Xiaofeng Gao
Keyword(s):  
Time Series ◽  
Urban Development ◽  
Linear Model ◽  
Urban Growth ◽  
Vegetation Type ◽  
Development Trend ◽  
Practical Significance ◽  
Model Parameters ◽  
Growth Trend ◽  
Development Trends

Uncontrolled urban growth detracts from healthy urban development. Understanding urban development trends and predicting future urban spatial states is of great practical significance. In order to comprehensively analyze urbanization and its effect on vegetation cover, we extracted urban development trends from time series DMSP/OLS NTL and NDVI data from 2000 to 2015, using a linear model fitting method. Six urban development trend types were identified by clustering the linear model parameters. The identified trend types were found to accurately reflect the on-ground conditions and changes in the Jinan area. For example, a high-density, stable urban type was found in the city center while a stable dense vegetation type was found in the mountains to the south. The SLEUTH model was used for urban growth simulation under three scenarios built on the urban development analysis results. The simulation results project a gentle urban growth trend from 2015 to 2030, demonstrating the prospects for urban growth from the perspective of environmental protection and conservative urban development.

Evaluation of Turkish social distancing measures on the spread of COVID-19 (Preprint)

2020 ◽  
Author(s):  
Irfan Civcir
Keyword(s):  
Linear Model ◽  
Model Parameters ◽  
Social Distancing ◽  
Slowing Down ◽  
Epidemic Size ◽  
Johns Hopkins University ◽  
The Government ◽  
Log Linear

UNSTRUCTURED The coronavirus disease (COVID-19) affecting across the globe. The government of different countries has adopted various policies to contain this epidemic and the most common were social distancing and lockdown. We use a simple log-linear model with intercept and trend break to evaluate whether the measures are effective preventing/slowing down the spread of the disease in Turkey. We estimate the model parameters from the Johns Hopkins University (2020) epidemic data between 15th March and 26th April 2020. Our analysis revealed that the measures can slow down the outbreak. We can reduce the epidemic size and prolong the time to arrive at the epidemic peak by seriously following the measures suggested by the authorities.

Kalman Filter and Model-Free Adaptive Control Theory Applied to the Unsteady Flow State Estimation of Product Pipelines

2020 ◽  
Author(s):  
Lei He ◽  
Kai Wen ◽  
Jing Gong
Keyword(s):  
Unsteady Flow ◽  
Linear Model ◽  
Flow Model ◽  
Transient Flow ◽  
Linear Method ◽  
Model Parameters ◽  
Flow State ◽  
Transient Pressure ◽  
Linear Flow ◽  
Model Based

Abstract The accurate online estimation of unsteady flow state provides important operation information for product pipelines real-time scheduling. In practice, affected by the parameter drift and observation noises, traditional estimation methods based on the first principle can hardly provide accurate results within acceptable time. The nonlinear and fast transient characteristics of pipeline flow make it difficult to realize on-line adaptive modification of model parameters. In order to meet the requirements of computational efficiency and accuracy simultaneously, this paper proposes a methodology with two-level adaptive adjustment to realize the digital twin of pipeline nonlinear transient flow process by using simplified linear flow model. In terms of improving computing efficiency, the linear flow model based on frequency response and difference transforming is established to process the on-line state estimation of transient flow. To reduce the deviation between the actual observed value and the linear model estimation, we first introduce mode-free adaptive control method as linear compensation of the reduced order unsteady flow model. The compact form dynamic linearization method has been adopted to design the virtual input of the linear flow model. To further improve the adaptability of the linear model, the model parameters are online adjusted by using the recursive least squares with forgetting factor method. The uncertainty of the model and the interference of observation noise is eliminated by adopting Kalman filter to the state space model based on modified linear model. The effectiveness of the proposed methodology is evaluated by applying to the digital twin process of a product pipeline transient pressure in a multistation pipeline. The results show that the proposed method can make transient pressure estimation of second-order linear model agree well with the value of nonlinear flow model even under unforeseen conditions and noise interference. The performance of the proposed method is better than model-based linear method, data-driven linear method and nonlinear method.

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