Estimation of aerodynamic parameters near stall using maximum likelihood and extreme learning machine-based methods

2020 ◽  
pp. 1-21
Author(s):  
H.O. Verma ◽  
N.K. Peyada
Keyword(s):  
Maximum Likelihood ◽  
Extreme Learning Machine ◽  
Unsteady Aerodynamics ◽  
Estimation Algorithm ◽  
Neural Model ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Aerodynamic Parameters ◽  
Learning Machine ◽  
And Control

ABSTRACT The stability and control derivatives are essential parameters in the flight operation of aircraft, and their determination is a routine task using classical parameter estimation methods based on maximum likelihood and least-squares principles. At high angle-of-attack, the unsteady aerodynamics may pose difficulty in aerodynamic structure determination, hence data-driven methods based on artificial neural networks could be an alternative choice for building models to characterise the behaviour of the system based on the measured motion and control variables. This research paper investigates the feasibility of using a recurrent neural model based on an extreme learning machine network in the modelling of the aircraft dynamics in a restricted sense for identification of the aerodynamic parameters. The recurrent extreme learning machine network is combined with the Gauss–Newton method to optimise the unknowns of the postulated aerodynamic model. The efficacy of the proposed estimation algorithm is studied using real flight data from a quasi-steady stall manoeuvre. Furthermore, the estimates are validated against the parameters estimated using the maximum likelihood method. The standard deviations of the estimates demonstrate the effectiveness of the proposed algorithm. Finally, the quantities regenerated using the estimates present good agreement with their corresponding measured values, confirming that a qualitative estimation can be obtained using the proposed estimation algorithm.

Parameter estimation of aircraft using extreme learning machine and Gauss-Newton algorithm

2019 ◽  
Vol 124 (1272) ◽  
pp. 271-295 ◽  
Author(s):  
H. O. Verma ◽  
N. K. Peyada
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Computational Cost ◽  
Feedforward Neural Network ◽  
Estimation Methods ◽  
Flight Data ◽  
Regression Methods ◽  
Directional Motion ◽  
Aerodynamic Parameters ◽  
Learning Machine

ABSTRACTThe research paper addresses the problem of estimating aerodynamic parameters using a Gauss-Newton-based optimisation method. The process of the optimisation method lies on the principle of minimising the residual error between the measured and simulated responses of the system. Usually, the simulated response is obtained by integrating the dynamic equations of the system, which is found to be susceptible to the initial values, and the integration method. With the advent of the feedforward neural network, the data-driven regression methods have been widely used for identification of the system. Among them, a variant of feedforward neural network, extreme learning machine, which has proven the performance in terms of computational cost, generalisation, and so forth, has been addressed to predict the responses in the present study. The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters. Furthermore, the estimates have been validated with the values of the classical estimation methods, such as the equation-error and filter-error methods. The sample standard deviations of the estimates demonstrate the effectiveness of the proposed method. Lastly, the proof-of-match exercise has been conducted with the other set of flight data to validate the estimated parameters.

scholarly journals A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4068
Author(s):  
Zheshuo Zhang ◽  
Jie Zhang ◽  
Jiawen Dai ◽  
Bangji Zhang ◽  
Hengmin Qi
Keyword(s):  
Unscented Kalman Filter ◽  
Time Estimation ◽  
Estimation Algorithm ◽  
Estimation Methods ◽  
Fusion Algorithm ◽  
State Variable ◽  
Current Estimation ◽  
Real Time Estimation ◽  
And Control ◽  
Independent Parameters

Vehicle parameters are essential for dynamic analysis and control systems. One problem of the current estimation algorithm for vehicles’ parameters is that: real-time estimation methods only identify parts of vehicle parameters, whereas other parameters such as suspension damping coefficients and suspension and tire stiffnesses are assumed to be known in advance by means of an inertial parameter measurement device (IPMD). In this study, a fusion algorithm is proposed for identifying comprehensive vehicle parameters without the help of an IPMD, and vehicle parameters are divided into time-independent parameters (TIPs) and time-dependent parameters (TDPs) based on whether they change over time. TIPs are identified by a hybrid-mass state-variable (HMSV). A dual unscented Kalman filter (DUKF) is applied to update both TDPs and online states. The experiment is conducted on a real two-axle vehicle and the test data are used to estimate both TIPs and TDPs to validate the accuracy of the proposed algorithm. Numerical simulations are performed to further investigate the algorithm’s performance in terms of sprung mass variation, model error because of linearization and various road conditions. The results from both the experiment and simulation show that the proposed algorithm can estimate TIPs as well as update TDPs and online states with high accuracy and quick convergence, and no requirement of road information.

scholarly journals New Lindley Half Cauchy Distribution: Theory and Applications

2020 ◽  
Vol 9 (4) ◽  
pp. 1-7
Keyword(s):  
Maximum Likelihood ◽  
Real Data ◽  
Cauchy Distribution ◽  
Least Square ◽  
Cumulative Distribution ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Model Parameters ◽  
Data Set ◽  
Von Mises

In this paper, we have defined a new two-parameter new Lindley half Cauchy (NLHC) distribution using Lindley-G family of distribution which accommodates increasing, decreasing and a variety of monotone failure rates. The statistical properties of the proposed distribution such as probability density function, cumulative distribution function, quantile, the measure of skewness and kurtosis are presented. We have briefly described the three well-known estimation methods namely maximum likelihood estimators (MLE), least-square (LSE) and Cramer-Von-Mises (CVM) methods. All the computations are performed in R software. By using the maximum likelihood method, we have constructed the asymptotic confidence interval for the model parameters. We verify empirically the potentiality of the new distribution in modeling a real data set.

scholarly journals Estimation of Critical Headway at Small Urban Roundabout

2020 ◽  
Vol 32 (1) ◽  
pp. 103-117
Author(s):  
Danijela Maslać ◽  
Dražen Cvitanić ◽  
Ivan Lovrić
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Project Design ◽  
Capacity Estimation ◽  
Local Conditions ◽  
Large Numbers ◽  
The World

Before choosing an intersection project design, an important step is to examine the justification of the construction on the basis of defined criteria. One of the key criteria is the analysis of capacity. Large numbers of roundabout capacity models are present in the world, most of them adapted to the conditions of the country they originate from and they need to be calibrated for local conditions. Key parameters for calibration are critical headway and follow-up headway. Follow-up headway can be measured directly in the field, while critical headway cannot be measured, but is estimated. Many critical headway estimation methods exist (over 30) and each of them provides different values. Different values of critical headway result in different capacity estimation values. This raises the question which method provides more realistic estimations under certain conditions. In this paper, four most frequently used critical headway estimation methods (Raff, Maximum likelihood method, Wu, Logit) were selected to be tested by comparison of theoretical capacity models and actual measured capacity at a small urban roundabout.

scholarly journals Modeling of Autonomous Underwater Vehicles with Multi-Propellers Based on Maximum Likelihood Method

2020 ◽  
Vol 8 (6) ◽  
pp. 407
Author(s):  
Feiyan Min ◽  
Guoliang Pan ◽  
Xuefeng Xu
Keyword(s):  
Maximum Likelihood ◽  
Controller Design ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Likelihood Method ◽  
Hydrodynamic Characteristics ◽  
Identification Algorithm ◽  
Modeling And Control ◽  
Good Convergence ◽  
And Control

The hydrodynamic characteristics of multi-propeller autonomous underwater vehicles (AUV) is usually complicated and it is difficult to obtain an accurate mathematical model. A modeling method based on CFD calculation and maximum likelihood identification algorithm is proposed for this problem. Firstly, rough hydrodynamic parameters of AUV hull are obtained by CFD calculation. Secondly, on the basis of rough parameters, a maximum likelihood identification algorithm is proposed to adjust the parameters and improve the model precision. Besides, the method to improve the convergence of identification algorithm is analyzed by considering the characteristics of AUV model structure. Finally, the identification algorithm and identification results were validated with experimental data. It was found that this method has good convergence and adaptability. In particular, the identification results of turning force and torque parameters are highly consistent in different identification experiments, which indicates that this method can well extract the maneuvering characteristics of AUVs, thus contributing to the controller design of AUVs. The research of this paper has potential application for the modeling and control of multi-propeller AUVs.

Different inference approaches for the estimators of the sushila distribution

2021 ◽  
Vol 16 (4) ◽  
pp. 251-260
Author(s):  
Marcos Vinicius de Oliveira Peres ◽  
Ricardo Puziol de Oliveira ◽  
Edson Zangiacomi Martinez ◽  
Jorge Alberto Achcar
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Bayesian Method ◽  
Weighted Least Squares ◽  
Computational Cost ◽  
Estimation Method ◽  
Computation Time ◽  
Ordinary Least Squares ◽  
Likelihood Method ◽  
Estimation Methods

In this paper, we order to evaluate via Monte Carlo simulations the performance of sample properties of the estimates of the estimates for Sushila distribution, introduced by Shanker et al. (2013). We consider estimates obtained by six estimation methods, the known approaches of maximum likelihood, moments and Bayesian method, and other less traditional methods: L-moments, ordinary least-squares and weighted least-squares. As a comparison criterion, the biases and the roots of mean-squared errors were used through nine scenarios with samples ranging from 30 to 300 (every 30rd). In addition, we also considered a simulation and a real data application to illustrate the applicability of the proposed estimators as well as the computation time to get the estimates. In this case, the Bayesian method was also considered. The aim of the study was to find an estimation method to be considered as a better alternative or at least interchangeable with the traditional maximum likelihood method considering small or large sample sizes and with low computational cost.

scholarly journals Applying Transformer Insulation Using Weibull Extended Distribution Based on Progressive Censoring Scheme

Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 100
Author(s):  
Hisham M. Almongy ◽  
Fatma Y. Alshenawy ◽  
Ehab M. Almetwally ◽  
Doaa A. Abdo
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Type Ii ◽  
Type Ii Censoring ◽  
Progressive Type ◽  
Transformer Insulation ◽  
Censoring Scheme ◽  
Progressive Type Ii Censoring

In this paper, the Weibull extension distribution parameters are estimated under a progressive type-II censoring scheme with random removal. The parameters of the model are estimated using the maximum likelihood method, maximum product spacing, and Bayesian estimation methods. In classical estimation (maximum likelihood method and maximum product spacing), we did use the Newton–Raphson algorithm. The Bayesian estimation is done using the Metropolis–Hastings algorithm based on the square error loss function. The proposed estimation methods are compared using Monte Carlo simulations under a progressive type-II censoring scheme. An empirical study using a real data set of transformer insulation and a simulation study is performed to validate the introduced methods of inference. Based on the result of our study, it can be concluded that the Bayesian method outperforms the maximum likelihood and maximum product-spacing methods for estimating the Weibull extension parameters under a progressive type-II censoring scheme in both simulation and empirical studies.

scholarly journals Comparison of Phase Estimation Methods for Quantitative Susceptibility Mapping Using a Rotating-Tube Phantom

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Kathryn E. Keenan ◽  
Ben P. Berman ◽  
Slávka Rýger ◽  
Stephen E. Russek ◽  
Wen-Tung Wang ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Data Acquisition ◽  
Relative Error ◽  
Region Growing ◽  
Susceptibility Mapping ◽  
Cumulative Distribution ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Phase Estimation ◽  
Quantitative Susceptibility Mapping

Quantitative Susceptibility Mapping (QSM) is an MRI tool with the potential to reveal pathological changes from magnetic susceptibility measurements. Before phase data can be used to recover susceptibility ( Δ χ ), the QSM process begins with two steps: data acquisition and phase estimation. We assess the performance of these steps, when applied without user intervention, on several variations of a phantom imaging task. We used a rotating-tube phantom with five tubes ranging from Δ χ = 0.05 ppm to Δ χ = 0.336  ppm. MRI data was acquired at nine angles of rotation for four different pulse sequences. The images were processed by 10 phase estimation algorithms including Laplacian, region-growing, branch-cut, temporal unwrapping, and maximum-likelihood methods, resulting in approximately 90 different combinations of data acquisition and phase estimation methods. We analyzed errors between measured and expected phases using the probability mass function and Cumulative Distribution Function. Repeatable acquisition and estimation methods were identified based on the probability of relative phase errors. For single-echo GRE and segmented EPI sequences, a region-growing method was most reliable with Pr (relative error <0.1) = 0.95 and 0.90, respectively. For multiecho sequences, a maximum-likelihood method was most reliable with Pr (relative error <0.1) = 0.97. The most repeatable multiecho methods outperformed the most repeatable single-echo methods. We found a wide range of repeatability and reproducibility for off-the-shelf MRI acquisition and phase estimation approaches, and this variability may prevent the techniques from being widely integrated in clinical workflows. The error was dominated in many cases by spatially discontinuous phase unwrapping errors. Any postprocessing applied on erroneous phase estimates, such as QSM’s background field removal and dipole inversion, would suffer from error propagation. Our paradigm identifies methods that yield consistent and accurate phase estimates that would ultimately yield consistent and accurate Δ χ estimates.

scholarly journals Comparing estimation methods of non-stationary errors-in-variables models

2019 ◽  
Vol 3 (1) ◽  
pp. 73-101 ◽  
Author(s):  
Naoto Kunitomo ◽  
Naoki Awaya ◽  
Daisuke Kurisu
Keyword(s):  
Maximum Likelihood ◽  
Measurement Errors ◽  
Likelihood Function ◽  
Asymptotic Properties ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Errors In Variables ◽  
Economic Time Series ◽  
Finite Sample ◽  
Ml Estimation

AbstractWe investigate the estimation methods of the multivariate non-stationary errors-in-variables models when there are non-stationary trend components and the measurement errors or noise components. We compare the maximum likelihood (ML) estimation and the separating information maximum likelihood (SIML) estimation. The latter was proposed by Kunitomo and Sato (Trend, seasonality and economic time series: the nonstationary errors-in-variables models. MIMS-RBP-SDS-3, MIMS, Meiji University. http://www.mims.meiji.ac.jp/, 2017) and Kunitomo et al. (Separating information maximum likelihood method for high-frequency financial data. Springer, Berlin, 2018). We have found that the Gaussian likelihood function can have non-concave shape in some cases and the ML method does work only when the Gaussianity of non-stationary and stationary components holds with some restrictions such as the signal–noise variance ratio in the parameter space. The SIML estimation has the asymptotic robust properties in more general situations. We explore the finite sample and asymptotic properties of the ML and SIML methods for the non-stationary errors-in variables models.

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