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Journal articles on the topic 'Initial values estimation'
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Wang, Zhenyu, Hana Sheikh, Kyongbum Lee, and Christos Georgakis. "Sequential Parameter Estimation for Mammalian Cell Model Based on In Silico Design of Experiments." Processes 6, no. 8 (July 24, 2018): 100. http://dx.doi.org/10.3390/pr6080100.
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Due to the complicated metabolism of mammalian cells, the corresponding dynamic mathematical models usually consist of large sets of differential and algebraic equations with a large number of parameters to be estimated. On the other hand, the measured data for estimating the model parameters are limited. Consequently, the parameter estimates may converge to a local minimum far from the optimal ones, especially when the initial guesses of the parameter values are poor. The methodology presented in this paper provides a systematic way for estimating parameters sequentially that generates better initial guesses for parameter estimation and improves the accuracy of the obtained metabolic model. The model parameters are first classified into four subsets of decreasing importance, based on the sensitivity of the model’s predictions on the parameters’ assumed values. The parameters in the most sensitive subset, typically a small fraction of the total, are estimated first. When estimating the remaining parameters with next most sensitive subset, the subsets of parameters with higher sensitivities are estimated again using their previously obtained optimal values as the initial guesses. The power of this sequential estimation approach is illustrated through a case study on the estimation of parameters in a dynamic model of CHO cell metabolism in fed-batch culture. We show that the sequential parameter estimation approach improves model accuracy and that using limited data to estimate low-sensitivity parameters can worsen model performance.
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Wu, Yunlong, Qing Zhang, and Shuxuan Zhang. "Fast Cylindrical Fitting Method Using Point Cloud’s Normals Estimation." Mathematical Problems in Engineering 2018 (December 12, 2018): 1–7. http://dx.doi.org/10.1155/2018/8904653.
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Cylindrical fitting is an essential step in Large Process Pipeline’s measurement process, and precision of initial values of cylindrical fitting is a key element in getting a correct fitting result. In order to get well initial values, covariance matrixes of all points in cylinder’s three-dimensional laser scanning point cloud should be firstly established to estimate normals of all points, and then cylinder’s axis vector can be calculated by using least squares method. Secondly, remaining parameters’ initial values of the cylinder can be got by coordinate transformation. Finally, Levenberg-Marquardt algorithm is used in iterative optimization process to get fitting result by using the above values as initial values. Experiments demonstrate that this method can get precise initial values of cylindrical fitting and improve the accuracy and speed of cylindrical fitting.
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Awoda, Murtadha, and Ramzy Ali. "Parameter Estimation of a Permanent Magnetic DC Motor." Iraqi Journal for Electrical and Electronic Engineering 15, no. 1 (June 1, 2019): 28–36. http://dx.doi.org/10.37917/ijeee.15.1.3.
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The identification of system parameters plays an essential role in system modeling and control. This paper presents a parameter estimation for a permanent magnetic DC motor using the simulink design optimization method. The parameter estimation may be represented as an optimization problem. Firstly, the initial values of the DC motor parameters are extracted using the dynamic model through measuring the values of voltage, current, and speed of the motor. Then, these values are used as an initial value for simulink design optimization. The experimentally inputoutput data can be collected using a suggested microcontroller based circuit that will be used later for estimating the DC motor parameters by building a simulink model. Two optimization algorithms are used, the pattern search and the nonlinear least square. The results show that the nonlinear least square algorithm gives a more accurate result that almost approaches to the actual measured speed response of the motor.
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Simonov, Andrey, Jean-Pierre Dubé, Günter Hitsch, and Peter Rossi. "State-Dependent Demand Estimation with Initial Conditions Correction." Journal of Marketing Research 57, no. 5 (August 14, 2020): 789–809. http://dx.doi.org/10.1177/0022243720941529.
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The authors analyze the initial conditions bias in the estimation of brand choice models with structural state dependence. Using a combination of Monte Carlo simulations and empirical case studies of shopping panels, they show that popular, simple solutions that misspecify the initial conditions are likely to lead to bias even in relatively long panel data sets. The magnitude of the bias in the state dependence parameter can be as large as a factor of 2–2.5. The authors propose a solution to the initial conditions problem that samples the initial states as auxiliary variables in a Markov chain Monte Carlo procedure. The approach assumes that the joint distribution of prices and consumer choices is in equilibrium, which is plausible for the mature consumer packaged goods products commonly used in empirical applications. In Monte Carlo simulations, the approach recovers the true parameter values even in relatively short panels. Finally, the authors propose a diagnostic tool that uses common, biased approaches to bound the values of the state dependence and construct a computationally light test for state dependence.
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Karim, Muhammad Ahsar, Agus Yodi Gunawan, Mochamad Apri, and Kuntjoro Adji Sidarto. "SOLVING A PARAMETER ESTIMATION PROBLEM OF GOODWIN MODEL WITH FUZZY INITIAL VALUES." Far East Journal of Mathematical Sciences (FJMS) 107, no. 2 (October 11, 2018): 321–38. http://dx.doi.org/10.17654/ms107020321.
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Mulry, Mary H., Stephen Kaputa, and Katherine J. Thompson. "Setting M-Estimation Parameters for Detection and Treatment of Influential Values." Journal of Official Statistics 34, no. 2 (June 1, 2018): 483–501. http://dx.doi.org/10.2478/jos-2018-0022.
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Abstract Recent research on the use of M-estimation methodology for detecting and treating verified influential values in economic surveys found that initial parameter settings affect effectiveness. In this article, we explore the basic question of how to develop initial settings for the M-estimation parameters. The economic populations that we studied are highly skewed and are consequently highly stratified. While we investigated settings for several parameters, the most challenging problem was to develop an “automatic” data-driven method for setting the initial value of the tuning constant φ, the parameter with the greatest influence on performance of the algorithm. Of all the methods that we considered, we found that methods defined in terms of the accuracy of published estimates can be implemented on a large scale and yielded the best performance. We illustrate the methodology with an empirical analysis of 36 consecutive months of data from 19 industries in the Monthly Wholesale Trade Survey.
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Johansen, Søren, and Morten Ørregaard Nielsen. "THE ROLE OF INITIAL VALUES IN CONDITIONAL SUM-OF-SQUARES ESTIMATION OF NONSTATIONARY FRACTIONAL TIME SERIES MODELS." Econometric Theory 32, no. 5 (May 11, 2015): 1095–139. http://dx.doi.org/10.1017/s0266466615000110.
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In this paper, we analyze the influence of observed and unobserved initial values on the bias of the conditional maximum likelihood or conditional sum-of-squares (CSS, or least squares) estimator of the fractional parameter,d, in a nonstationary fractional time series model. The CSS estimator is popular in empirical work due, at least in part, to its simplicity and its feasibility, even in very complicated nonstationary models.We consider a process,Xt, for which data exist from some point in time, which we call –N0+ 1, but we only start observing it at a later time,t= 1. The parameter (d,μ,σ2) is estimated by CSS based on the model${\rm{\Delta }}_0^d \left( {X_t - \mu } \right) = \varepsilon _t ,t = N + 1, \ldots ,N + T$, conditional onX1,...,XN. We derive an expression for the second-order bias of$\hat d$as a function of the initial values,Xt,t= –N0+ 1,...,N, and we investigate the effect on the bias of setting aside the firstNobservations as initial values. We compare$\hat d$with an estimator,$\hat d_c $, derived similarly but by choosingμ=C. We find, both theoretically and using a data set on voting behavior, that in many cases, the estimation of the parameterμpicks up the effect of the initial values even for the choiceN= 0.IfN0= 0, we show that the second-order bias can be completely eliminated by a simple bias correction. If, on the other hand,N0> 0, it can only be partly eliminated because the second-order bias term due to the initial values can only be diminished by increasingN.
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Journal, Baghdad Science. "A Comparison of the Methods for Estimation of Reliability Function for Burr-XII Distribution by Using Simulation." Baghdad Science Journal 10, no. 1 (March 3, 2013): 85–96. http://dx.doi.org/10.21123/bsj.10.1.85-96.
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This deals with estimation of Reliability function and one shape parameter (?) of two- parameters Burr – XII , when ?(shape parameter is known) (?=0.5,1,1.5) and also the initial values of (?=1), while different sample shze n= 10, 20, 30, 50) bare used. The results depend on empirical study through simulation experiments are applied to compare the four methods of estimation, as well as computing the reliability function . The results of Mean square error indicates that Jacknif estimator is better than other three estimators , for all sample size and parameter values
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Panić, Branislav, Jernej Klemenc, and Marko Nagode. "Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation." Mathematics 8, no. 3 (March 7, 2020): 373. http://dx.doi.org/10.3390/math8030373.
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A commonly used tool for estimating the parameters of a mixture model is the Expectation–Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density–estimation datasets and image–segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.
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Zaika, Yury V. "Interval estimates of functionals in time-delay systems with uncertainty." International Journal of Mathematics and Mathematical Sciences 2003, no. 56 (2003): 3573–90. http://dx.doi.org/10.1155/s0161171203203264.
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We consider a linear dynamical system with delay and uncertainty in initial data and movement and measurement equations. We present an algorithm of estimating an interval of possible values of functionals on solutions. We construct suboptimal weight functions in integral observation operators to minimize a sure estimation.
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Matsunaga, Eiichi, and Tomomasa Ohkubo. "Using the Characteristic Equation to Estimate the Initial Values for Numerical Forecasts." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 7 (December 20, 2016): 1147–51. http://dx.doi.org/10.20965/jaciii.2016.p1147.
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Japan is an island nation that experiences frequent earthquakes. When an earthquake occurs, it is important to forecast its resultant tsunami: its size, location, time of arrival, etc. These forecasts are made using numerical simulations. The initial conditions are very important for numerical simulations, but the small number of tide stations makes it difficult to make highly precise forecasts. The distance between stations is normally several tens of km, and this lowers the precision of the initial data afforded by them. It is therefore common to use data interpolated from the sparse observation data at timet=0. Even so, high-resolution interpolation cannot be expected since the original data is of poor quality. In addition, the interpolated values may not be physically valid because the governing equation may not have been considered when the data were interpolated. We therefore propose a new method of estimating the initial value by using a characteristic equation. In this method, we replace the spatial resolution with time resolution. This results in a high-resolution initial value because the same place is measured more than once. In addition, the characteristic equation is based on the governing equation. Therefore, in this method, an accurate estimation of initial value is considered to be possible. In this paper, we show two applications of this approach, one for a dimensional shallow water wave equation and one for Euler’s equation. The shallow water wave equation is for the tsunami, and the Euler equation is the governing equation of the numerical weather forecast.
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Baszczyńska, Aleksandra Katarzyna. "One Value of Smoothing Parameter vs Interval of Smoothing Parameter Values in Kernel Density Estimation." Acta Universitatis Lodziensis. Folia Oeconomica 6, no. 332 (February 2, 2018): 73–86. http://dx.doi.org/10.18778/0208-6018.332.05.
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Ad hoc methods in the choice of smoothing parameter in kernel density estimation, although often used in practice due to their simplicity and hence the calculated efficiency, are characterized by quite big error. The value of the smoothing parameter chosen by Silverman method is close to optimal value only when the density function in population is the normal one. Therefore, this method is mainly used at the initial stage of determining a kernel estimator and can be used only as a starting point for further exploration of the smoothing parameter value. This paper presents ad hoc methods for determining the smoothing parameter. Moreover, the interval of smoothing parameter values is proposed in the estimation of kernel density function. Basing on the results of simulation studies, the properties of smoothing parameter selection methods are discussed.
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Kim, Namhoon, Sangho Baek, and Gihong Kim. "Absolute IOP/EOP Estimation Models without Initial Information of Various Smart City Sensors." Sensors 23, no. 2 (January 9, 2023): 742. http://dx.doi.org/10.3390/s23020742.
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In smart cities, a large amount of optical camera equipment is deployed and used. Closed-circuit television (CCTV), unmanned aerial vehicles (UAVs), and smartphones are some examples of such equipment. However, additional information about these devices, such as 3D position, orientation information, and principal distance, is not provided. To solve this problem, the structured mobile mapping system point cloud was used in this study to investigate methods of estimating the principal point, position, and orientation of optical sensors without initial given values. The principal distance was calculated using two direct linear transformation (DLT) models and a perspective projection model. Methods for estimating position and orientation were discussed, and their stability was tested using real-world sensors. When the perspective projection model was used, the camera position and orientation were best estimated. The original DLT model had a significant error in the orientation estimation. The correlation between the DLT model parameters was thought to have influenced the estimation result. When the perspective projection model was used, the position and orientation errors were 0.80 m and 2.55°, respectively. However, when using a fixed-wing UAV, the estimated result was not properly produced owing to ground control point placement problems.
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Cheng, Jun, Liyan Zhang, and Qihong Chen. "Fast Monocular Visual-Inertial Initialization with an Improved Iterative Strategy." Journal of Sensors 2021 (May 27, 2021): 1–18. http://dx.doi.org/10.1155/2021/5565158.
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The initialization process has a great effect on the performance of the monocular visual inertial simultaneous localization and mapping (VI-SLAM) system. The initial estimation is usually solved by least squares such as the Gauss-Newton (G-N) algorithm, but the large iteration increment might lead to the slow convergence or even divergence. In order to solve this problem, an improved iterative strategy for initial estimation is proposed. The methodology of our initialization can be divided into four steps: Firstly, the pure visual ORB-SLAM model is utilized to make all variables observable. Secondly, the IMU preintegration technology is adopted for IMU-camera frequency alignment at the same time with key frame generation. Thirdly, an improved iterative strategy which is based on the trust region is introduced for the gyroscope bias estimation as well as the gravity direction is refined. Finally, the accelerometer bias and visual scale are estimated on the basis of previous estimations. Experimental results on the public datasets show that the estimation of initial values can be converged faster, as well as the velocity and pose of sensor suite can be estimated more accurately than the original method.
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Chauvet, Antoine, Yoshihiro Sugaya, Tomo Miyazaki, and Shinichiro Omachi. "Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation." Applied Sciences 10, no. 2 (January 20, 2020): 729. http://dx.doi.org/10.3390/app10020729.
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This study proposes a lightweight solution to estimate affine parameters in affine motion compensation. Most of the current approaches start with an initial approximation based on the standard motion estimation, which only estimates the translation parameters. From there, iterative methods are used to find the best parameters, but they require a significant amount of time. The proposed method aims to speed up the process in two ways, first, skip evaluating affine prediction when it is likely to bring no encoding efficiency benefit, and second, by estimating better initial values for the iteration process. We use the optical flow between the reference picture and the current picture to estimate quickly the best encoding mode and get a better initial estimation. We achieve a reduction in encoding time over the reference of half when compared to the state of the art, with a loss in efficiency below 1%.
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Lin, Pei-Wei, and Gang-Len Chang. "Modeling measurement errors and missing initial values in freeway dynamic origin–destination estimation systems." Transportation Research Part C: Emerging Technologies 14, no. 6 (December 2006): 384–402. http://dx.doi.org/10.1016/j.trc.2006.10.002.
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Matsumoto-Moriyama, Masao, and Kiyoshi Tsuchiya. "Selection method of initial guess values for surface temperature estimation from the satellite data." Advances in Space Research 14, no. 3 (March 1994): 37–40. http://dx.doi.org/10.1016/0273-1177(94)90189-9.
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Polanski, Andrzej, Michal Marczyk, Monika Pietrowska, Piotr Widlak, and Joanna Polanska. "Initializing the EM Algorithm for Univariate Gaussian, Multi-Component, Heteroscedastic Mixture Models by Dynamic Programming Partitions." International Journal of Computational Methods 15, no. 03 (April 25, 2018): 1850012. http://dx.doi.org/10.1142/s0219876218500123.
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Setting initial values of parameters of mixture distributions estimated by using the EM recursive algorithm is very important to the overall quality of estimation. None of the existing methods are suitable for heteroscedastic mixtures with a large number of components. We present relevant novel methodology of estimating the initial values of parameters of univariate, heteroscedastic Gaussian mixtures, on the basis of dynamic programming partitioning of the range of observations into bins. We evaluate variants of the dynamic programming method corresponding to different scoring functions for partitioning. We demonstrate the superior efficiency of the proposed method compared to existing techniques for both simulated and real datasets.
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С. И., Носков,, and Базилевский, М. П. "Multiple Lv-estimation of Linear Regression Models." Успехи кибернетики / Russian Journal of Cybernetics, no. 4(12) (December 28, 2022): 32–40. http://dx.doi.org/10.51790/2712-9942-2022-3-4-04.
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для оценки моделей множественной линейной регрессии существует много различных математических методов: наименьших квадратов, модулей, антиробастного оценивания, Lv-оценивания, множественного оценивания. Целью данной работы является обобщение указанных методов оценивания единой функцией потерь. Сначала была сформулирована задача оценивания, в которой в качестве критериев минимизации выступают критерии для антиробастного и Lv-оценивания. Недостатком сформулированной задачи является то, что для ее численного решения затруднительно определять начальные значения параметров, поскольку переменные могут иметь разные масштабы. Кроме того, функция потерь для этой задачи является неоднородной, что также затрудняет процесс оценивания. Для решения этих проблем введен новый критерий, равный критерию антиробастного оценивания, возведенному в степень v. С помощью него и функции потерь для Lv-оценивания сформулирована задача множественного Lv-оценивания. Функционал этой задачи является однородным, поэтому для проведения множественного Lv-оценивания целесообразно нормировать исходные переменные и переходить к оценкам стандартизованной линейной регрессии. Предложен алгоритм, по которому рекомендуется проводить множественное Lv-оценивание. В результате проведения множественного Lv-оценивания формируется множество, содержащее оценки линейной регрессии, полученные как известными методами, так и новыми. Правильный выбор наилучших из полученного множества оценок пока остается открытой научной задачей. С помощью предложенного множественного Lv-оценивания успешно решена задача моделирования железнодорожных пассажирских перевозок Иркутской области. there are many methods for estimating multiple linear regression models: ordinary least squares, least absolute deviations, anti-robust estimation, Lv-estimation, and multiple estimations. The purpose of this work is to generalize these methods by a loss function. First, an estimation problem was formulated where the minimization criteria are the anti-robust and Lv-estimations. The disadvantage of this problem statement is that it is difficult to determine the initial values of the parameters for a numerical solution, since the variables may have different scales. Besides, the loss function is non-uniform, which also complicates the estimation. To solve these problems, we introduced a new criterion, equal to the anti-robust estimation criterion raised to the power v. We stated the problem of multiple Lv-estimation using the new criterion and the loss function. The functional of this problem is homogeneous, therefore, for multiple Lv-estimations, it is advisable to normalize the initial variables and then apply the standardized linear regression estimates. We also developed an algorithm for multiple Lv-estimations. A result of such estimations is a set containing linear regression estimates obtained both by the existing and new methods. The optimal choice of the best estimates from the set of estimates remains an open problem. We successfully simulated the passenger railway traffic in the Irkutsk region with the proposed multiple Lv-estimations.
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An, Lihua, S. Ejaz Ahmed, and Adnan Ali. "Tumor Growth Rate Approximation-Assisted Estimation." Cancer Informatics 2 (January 2006): 117693510600200. http://dx.doi.org/10.1177/117693510600200003.
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From tumor to tumor, there is a great variation in the proportion of cancer cells growing and making daughter cells that ultimately metastasize. The differential growth within a single tumor, however, has not been studied extensively and this may be helpful in predicting the aggressiveness of a particular cancer type. The estimation problem of tumor growth rates from several populations is studied. The baseline growth rate estimator is based on a family of interacting particle system models which generalize the linear birth process as models of tumor growth. These interacting models incorporate the spatial structure of the tumor in such a way that growth slows down in a crowded system. Approximation-assisted estimation strategy is proposed when initial values of rates are known from the previous study. Some alternative estimators are suggested and the relative dominance picture of the proposed estimator to the benchmark estimator is investigated. An over-riding theme of this article is that the suggested estimation method extends its traditional counterpart to non-normal populations and to more realistic cases.
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Moumni, Hasna, Karima Sebari, Laila Stour, and Abdellatif Ahbari. "Estimation of the HEC-HMS model parameters in data-scarce regions. Application to the Ouergha watershed (Sebou, Morocco)." E3S Web of Conferences 314 (2021): 05002. http://dx.doi.org/10.1051/e3sconf/202131405002.
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The availability, accessibility and quality of data are significant obstacles to hydrological modelling. Estimating the initial values of the hydrological model´’ ’s parameters is a laborious and determining task requiring much attention. Geographic information systems (GIS) and spatial remote sensing are prometting tools for processing and collecting data. In this work, we use an innovative approach to estimate the HEC-HMS hydrological model parameters from the soil map of Africa (250m), the land use map GLC30, the depth to bedrock map, the digital elevation model and observed flow data. The estimation approach is applied to the Ouergha basin (Sebou, Morocco). The proposed approach’s interest is to feed the HEC-HMS hydrological model with initial values of parameters close to the study area reality instead of using random parameters.
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Al-Sultany, Shurooq Ahmed. "Constructing a New Exponentiated Family Distribution with Reliability Estimation." Al-Mustansiriyah Journal of Science 29, no. 2 (November 17, 2018): 155. http://dx.doi.org/10.23851/mjs.v29i2.205.
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This paper deals with using one method for transforming two parameters given distribution to another form with three parameters distribution, through using idea of reparameterization with powering the given cumulative distribution function by new parameter, where this work gives a new family through using new parameter which is necessary for generating values of the r.v from given CDF through smoothing the values of the given random variable to obtain new values of r.v using three set of parameters rather than two. The new model Frechet p.d.f is obtained and also its Cumulative distribution function is found then we apply three methods of estimation (which are Maximum likelihood , moments estimator , and the third method is depend on using least square regression approach). Different set of initial values of parameters ( , , ) and different samples size (n=25,50,75,100). The simulation procedure is done using matlab-R2014b, and results are compared using integrated Mean square error.
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Binfan Liu and J. Si. "Error estimation of recurrent neural network models trained on a finite set of initial values." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 44, no. 11 (1997): 1086–89. http://dx.doi.org/10.1109/81.641775.
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Zhao, Lijuan, Yongqian Li, and Zhiniu Xu. "A fast and high accurate initial values obtainment method for Brillouin scattering spectrum parameter estimation." Sensors and Actuators A: Physical 210 (April 2014): 141–46. http://dx.doi.org/10.1016/j.sna.2014.02.022.
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Zhu, Ao, Shu Ying Wang, and Yong Zhen Peng. "A Simple Soft Sensing Method Based on DO Simulation for COD/OUR/Ammonia/Nitrite/Nitrate in a Constant Aerotion SBR." Advanced Materials Research 588-589 (November 2012): 64–67. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.64.
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Dissolved Oxygen (DO) is one of critical factors influencing substrate degradation and nitrification rates in activated sludge process. A two-step nitrification model describing the aeration period of the sequencing batch reactor (SBR) operation is developed. Parameter estimation of this model using the DO data determine the stoichiometry and kinetic parameters as well as the initial value of main nutrients, including readily biodegradable substrate, ammonia, nitrite and nitrate. Only if the parameter estimation is implemented, reaction related substrates can be predicted if new initial values are given. With optimal DO data, this paper pointed out that without initial value monitoring, both of the parameter estimation and prediction of the main nutrients profiles can be done through a simple method based on a global parameter estimation algorithm.
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Martín, Jacinto, María Isabel Parra, Mario Martínez Pizarro, and Eva L. Sanjuán. "Baseline Methods for Bayesian Inference in Gumbel Distribution." Entropy 22, no. 11 (November 7, 2020): 1267. http://dx.doi.org/10.3390/e22111267.
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Usual estimation methods for the parameters of extreme value distributions only employ a small part of the observation values. When block maxima values are considered, many data are discarded, and therefore a lot of information is wasted. We develop a model to seize the whole data available in an extreme value framework. The key is to take advantage of the existing relation between the baseline parameters and the parameters of the block maxima distribution. We propose two methods to perform Bayesian estimation. Baseline distribution method (BDM) consists in computing estimations for the baseline parameters with all the data, and then making a transformation to compute estimations for the block maxima parameters. Improved baseline method (IBDM) is a refinement of the initial idea, with the aim of assigning more importance to the block maxima data than to the baseline values, performed by applying BDM to develop an improved prior distribution. We compare empirically these new methods with the Standard Bayesian analysis with non-informative prior, considering three baseline distributions that lead to a Gumbel extreme distribution, namely Gumbel, Exponential and Normal, by a broad simulation study.
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Yaacob, M. S., H. Jamaluddin, and K. C. Wong. "Fuzzy logic approach for estimation of longitudinal aircraft parameters." Aeronautical Journal 106, no. 1065 (November 2002): 585–94. http://dx.doi.org/10.1017/s0001924000011593.
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AbstractThe use of rule-based fuzzy logic system for estimating the stability and control derivatives for the longitudinal aircraft motion is proposed. The capabilities of the fuzzy logic system in estimating both the short-period and the phugoid mode of motions are explored. The flight data used in the estimation process were generated using the three nonlinear longitudinal equation of motion for a small remotely piloted vehicle with all the aerodynamic coefficients obtained from the wind-tunnel tests. The preferred method of perturbation of the aircraft elevator for data collection is also highlighted. The stability and control derivatives are estimated as the change in the aerodynamic force or moment due to small variation in one of the motion or control variables about its nominal value when the rest of the variables are held constant at their respective nominal values. The changes in the aerodynamic force and moment are predicted using the fuzzy logic system. The results show that the fuzzy logic system has a good potential as alternative tools for parameter estimation from flight data. The proposed method does not require any guesses of the initial values of the flight parameters.
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Yaacob, M. S., H. Jamaluddin, and K. C. Wong. "Fuzzy logic approach for estimation of longitudinal aircraft parameters." Aeronautical Journal 106, no. 1065 (November 2002): 585–94. http://dx.doi.org/10.1017/s0001924000018248.
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AbstractThe use of rule-based fuzzy logic system for estimating the stability and control derivatives for the longitudinal aircraft motion is proposed. The capabilities of the fuzzy logic system in estimating both the short-period and the phugoid mode of motions are explored. The flight data used in the estimation process were generated using the three nonlinear longitudinal equation of motion for a small remotely piloted vehicle with all the aerodynamic coefficients obtained from the wind-tunnel tests. The preferred method of perturbation of the aircraft elevator for data collection is also highlighted. The stability and control derivatives are estimated as the change in the aerodynamic force or moment due to small variation in one of the motion or control variables about its nominal value when the rest of the variables are held constant at their respective nominal values. The changes in the aerodynamic force and moment are predicted using the fuzzy logic system. The results show that the fuzzy logic system has a good potential as alternative tools for parameter estimation from flight data. The proposed method does not require any guesses of the initial values of the flight parameters.
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Jacquez, J. A., and T. Perry. "Parameter estimation: local identifiability of parameters." American Journal of Physiology-Endocrinology and Metabolism 258, no. 4 (April 1, 1990): E727—E736. http://dx.doi.org/10.1152/ajpendo.1990.258.4.e727.
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For biological systems one often cannot set up experiments to measure all of the state variables. If only a subset of the state variables can be measured, it is possible that some of the system parameters cannot influence the measured state variables or that they do so in combinations that do not define the parameters' effects separately. Such parameters are unidentifiable and are in theory unestimable. Given a model of the system, linear or nonlinear, and initial estimates of the values of all parameters, we exhibit a simple theory and describe a program for checking the local identifiability of the parameters at the initial estimates for given experiments on the model. The program, IDENT, is available from the authors.
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Simoni, Luca, Manuel Beschi, Giovanni Legnani, and Antonio Visioli. "Modelling the temperature in joint friction of industrial manipulators." Robotica 37, no. 5 (November 10, 2017): 906–27. http://dx.doi.org/10.1017/s0263574717000509.
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SummaryIn this paper, a new model for joint dynamic friction of industrial robot manipulators is presented. In particular, the effects of the temperature in the joints are considered. A polynomial-based model is proposed and the parameter estimation is performed without the need of a joint temperature sensor. The use of an observer is then proposed to compensate for the uncertainty in the initial estimation of the temperature value. A large experimental campaign show that the model, in spite of the simplifying assumptions made, is effective in estimating the joint temperature and therefore the friction torque during the robot operations, even for values of velocities that have not been previously employed.
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Paizi, Koorosh, Hossein Parsaei, and Mohammad Mehdi Movahedi. "A MULTIPLE MODEL ALGORITHM FOR ESTIMATING MOTOR UNIT FIRING PATTERN STATISTICS." Biomedical Engineering: Applications, Basis and Communications 30, no. 06 (November 29, 2018): 1850047. http://dx.doi.org/10.4015/s1016237218500473.
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The firing patterns and recruitment behavior of motor units (MUs) during a muscle contraction can be used in diagnosing neuromuscular disorder, studying motor control, improving the performance of electromyography (EMG) signal decomposition, and assessing the validity of MU potential trains extracted by an EMG decomposition algorithm. However, MU firing patterns extracted via EMG decomposition might contain several missed or erroneous that can lead to misleading conclusion. In this paper, we presented a multiple model estimation system (MMES) for estimating the mean ([Formula: see text]) and standard deviation ([Formula: see text]) of inter-discharge intervals (IDIs) of a MU. The presented MMES aggregates an existing error-filtered estimation (EFE) algorithm and a multiple linear regression model to estimate both [Formula: see text] and [Formula: see text]. The MMES estimates these two parameters using 10 features extracted from given IDIs and initial estimation of [Formula: see text] and [Formula: see text] values provided by EFE algorithm. Evaluation results using both simulated and real IDIs revealed that MMES performed better than EFE algorithm in estimating both [Formula: see text] and [Formula: see text] values in terms of root mean square error (RMSE), estimating variance and the range of estimated values. The RMSE values for MMES in estimating [Formula: see text] for simulated and real IDIs, respectively, were [Formula: see text] and [Formula: see text] that are statistically lower than that of EFE algorithm with RMSE [Formula: see text] and [Formula: see text]. In estimating [Formula: see text] in simulated and real IDIs, RMSE values of MMES, respectively were [Formula: see text] and [Formula: see text] that was significantly lower than those values obtained for EFE [Formula: see text] (for simulated IDIs) and [Formula: see text] (for real IDIs). More importantly, MMES outperformed EFE algorithm for real train with right-skewed IDI distribution. Consequently, MMES is more accurate, reliable and consistent than EFE algorithm for estimating IDI mean and standard deviation.
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Sichangi, Arthur, Lei Wang, and Zhidan Hu. "Estimation of River Discharge Solely from Remote-Sensing Derived Data: An Initial Study Over the Yangtze River." Remote Sensing 10, no. 9 (August 31, 2018): 1385. http://dx.doi.org/10.3390/rs10091385.
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A novel approach has been developed to estimating river discharge solely using satellite-derived parameters. The temporal river width observations from Moderate Resolution Imaging Spectroradiometer (MODIS), made at two stream segments a distance apart, are plotted to identify the time lag. The river velocity estimate is then computed using the time lag and distance between the width measurement locations, producing a resultant velocity of 0.96 m/s. The estimated velocity is comparable to that computed from in situ gauge-observed data. An empirical relationship is then utilized to estimate river depth. In addition, the channel condition values published in tables are used to estimate the roughness coefficient. The channel slope is derived from the digital elevation model averaged over a river section approximately 516 km long. Finally, the temporal depth changes is captured by adjusting the estimated depth to the Envisat satellite altimetry -derived water level changes, and river width changes from Landsat ETM+. The newly developed procedure was applied to two river sites for validation. In both cases, the river discharges were estimated with reasonable accuracy (with Nash–Sutcliffe values >0.50). The performance evaluation of discharge estimation using satellite-derived parameters was also analyzed. Since the methodology for estimating discharge is solely dependent on global satellite datasets, it represents a promising technique for use on rivers worldwide.
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OKOROKOV, V. A. "EVENT SHAPES FOR HADRONIC FINAL STATE: EXPERIMENTAL REVIEW." International Journal of Modern Physics A 27, no. 08 (March 30, 2012): 1250037. http://dx.doi.org/10.1142/s0217751x12500376.
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The analysis is presented for the first moments of collective observable distributions in two-jet events for various interaction types and for wide initial energy range. These observables include sphericity, thrust, components of transverse particle momentum, alignment and planarity. Database of experimental results created in the framework of the paper includes data for all interactions. Energy dependencies of average values for collective observables except components of transverse momentum show universal behavior for various interactions. Particle transverse momentum as well as its components increase faster for e+e- interaction with growth of [Formula: see text], than that for other interactions. Empirical analytical functions are suggested for description of energy dependence for all collective observables under study with exception of infrared-stable thrust variable. Energy dependence for average thrust is compared with QCD predictions including perturbative part and analytical phenomenological corrections which account for nonperturbative effects. Dispersive model and single dressed gluon approximation are considered for description of energy dependence of first moment of thrust distribution and estimation of strong coupling constant for various interactions as well as for joint sample. The dispersive model allow to describe average thrust versus initial energy in wide range of [Formula: see text] down to strongly nonperturbative domain [Formula: see text] at qualitative level at least. Study of event shape observables allows to obtain estimations of αS(MZ) which are in reasonable agreement both with world average value and with results extracted in the framework of other methods. Using suggested analytical fitted functions some estimations of values of collective parameters under study have been obtained for present and future facilities. In TeV energy domain average values of collective observables either depend on [Formula: see text] weakly or do not depend on initial energy at all within errors. Thus, the TeV scale can be considered as an estimation of the low boundary of asymptotic region for traditional collective parameters. Usually, multiplicity dependence of collective observables under consideration agree with power function in energy domain [Formula: see text] at qualitative level at least. Behavior of sphericity versus multiplicity and comparison of experimental results with model calculations allow to suggest that the universal estimation of the low energy boundary for experimental appearance of event jet structure in multiparticle production processes is [Formula: see text].
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Yang, Fan, Guoyu Lin, and Weigong Zhang. "Terrain classification for terrain parameter estimation based on a dynamic testing system." Sensor Review 35, no. 4 (September 21, 2015): 329–39. http://dx.doi.org/10.1108/sr-01-2015-0003.
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Purpose – This paper aims to gain the real-time terrain parameters of the battlefield for the evaluation of military vehicle trafficability. In military missions, improvements in vehicle mobility have the potential to greatly increase the military operational capacity, in which vehicle trafficability plays a significant role. Design/methodology/approach – In this framework, an online terrain parameter estimation method based on the Gauss-Newton algorithm is proposed to estimate the primary terrain mechanical parameters. Good estimation results are indicated, unless the initial values involved are properly selected. Correspondingly, a method of terrain classification is then presented to contribute to the selection of the initial values. This method uses the wavelet packet transform technique for feature extraction and adopts the support vector machine algorithm for terrain classification. Once the terrain type is identified, advices can be given on the initial value selection referring to the empirical terrain parameters. Findings – On the basis of a dynamic testing system suitable for real military vehicles, the proposed algorithms are validated. High estimation accuracy of the terrain parameters is indicated on sandy loam, and good classification performance is demonstrated on four tested terrains. Originality/value – The presented algorithm outperforms the existing methods, which not only realizes the online terrain parameter estimation but also develops the estimation accuracy. Moreover, its effectiveness is confirmed by real vehicle tests in practice.
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Nie, S., J. Zhu, and Y. Luo. "Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments." Hydrology and Earth System Sciences 15, no. 8 (August 3, 2011): 2437–57. http://dx.doi.org/10.5194/hess-15-2437-2011.
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Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.
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Cordero, Alicia, Ivan Girona, and Juan R. Torregrosa. "A Variant of Chebyshev’s Method with 3αth-Order of Convergence by Using Fractional Derivatives." Symmetry 11, no. 8 (August 6, 2019): 1017. http://dx.doi.org/10.3390/sym11081017.
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In this manuscript, we propose several iterative methods for solving nonlinear equations whose common origin is the classical Chebyshev’s method, using fractional derivatives in their iterative expressions. Due to the symmetric duality of left and right derivatives, we work with right-hand side Caputo and Riemann–Liouville fractional derivatives. To increase as much as possible the order of convergence of the iterative scheme, some improvements are made, resulting in one of them being of 3 α -th order. Some numerical examples are provided, along with an study of the dependence on initial estimations on several test problems. This results in a robust performance for values of α close to one and almost any initial estimation.
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Garbar, S. V. "Dependency of regret on accuracy of variance estimation for different versions of UCB strategy for Gaussian multi-armed bandits." Journal of Physics: Conference Series 2052, no. 1 (November 1, 2021): 012013. http://dx.doi.org/10.1088/1742-6596/2052/1/012013.
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Abstract We consider two variations of upper confidence bound strategy for Gaussian two-armed bandits. Rewards for the arms are assumed to have unknown expected values and unknown variances. It is demonstrated that expected regret values for both discussed strategies are continuous functions of reward variance. A set of Monte-Carlo simulations was performed to show the nature of the relation between variance estimation and losses. It is shown that the regret grows only slightly when the estimation error is fairly large, which allows to estimate the variance during the initial steps of the control and stop this estimation later.
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Scarpa, Federico, and Luca A. Tagliafico. "Thermophysical Property Estimation by Transient Experiments: The Effect of a Biased Initial Temperature Distribution." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/494051.
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The identification of thermophysical properties of materials in dynamic experiments can be conveniently performed by the inverse solution of the associated heat conduction problem (IHCP). The inverse technique demands the knowledge of the initial temperature distribution within the material. As only a limited number of temperature sensors (or no sensor at all) are arranged inside the test specimen, the knowledge of the initial temperature distribution is affected by some uncertainty. This uncertainty, together with other possible sources of bias in the experimental procedure, will propagate in the estimation process and the accuracy of the reconstructed thermophysical property values could deteriorate. In this work the effect on the estimated thermophysical properties due to errors in the initial temperature distribution is investigated along with a practical method to quantify this effect. Furthermore, a technique for compensating this kind of bias is proposed. The method consists in including the initial temperature distribution among the unknown functions to be estimated. In this way the effect of the initial bias is removed and the accuracy of the identified thermophysical property values is highly improved.
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Cari, Elmer P. T., Edson A. R. Theodoro, Ana P. Mijolaro, Newton G. Bretas, and Luis F. C. Alberto. "Trajectory Sensitivity Method and Master-Slave Synchronization to Estimate Parameters of Nonlinear Systems." Mathematical Problems in Engineering 2009 (2009): 1–14. http://dx.doi.org/10.1155/2009/387317.
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A combination of trajectory sensitivity method and master-slave synchronization was proposed to parameter estimation of nonlinear systems. It was shown that master-slave coupling increases the robustness of the trajectory sensitivity algorithm with respect to the initial guess of parameters. Since synchronization is not a guarantee that the estimation process converges to the correct parameters, a conditional test that guarantees that the new combined methodology estimates the true values of parameters was proposed. This conditional test was successfully applied to Lorenz's and Chua's systems, and the proposed parameter estimation algorithm has shown to be very robust with respect to parameter initial guesses and measurement noise for these examples.
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Frulla, Giacomo. "Didactical Tool for Wing Weight Estimation in a Preliminary Aircraft Design Stage." WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION 18 (July 23, 2021): 78–85. http://dx.doi.org/10.37394/232010.2021.18.9.
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Aircraft preliminary design requires a lot of complex evaluations and assumptions related to design variables that are not completely known at a very initial stage. Didactical activity becomes unclear since students ask for precise values in the starting point. A tentative in providing a simple tool for wing weight estimation is presented for overcoming these common difficulties and explaining the following points: a) the intrinsic iterative nature of the preliminary design stage, b) provide useful and realistic calculation for the wing weight with very simple assumption not covered by cumbersome calculations and formulas. The purpose of the paper is to provide a didactic tool to facilitate the understanding of some steps in estimating wing weight at the preliminary design level. The problems of identifying the main variables for the initial estimation is dealt with and specifi aspects that are usually hidden by the complexity of the involved disciplines and by the usual calculation methods applied in structural design are pointed out. The procedure is addressed to highlight main steps in wing weight estimation for straight wing weight to highlight the main steps in estimating the wing weight for a general aviation straight wing aircraft at the preliminary design stage. The effect of the main variables on the wing weight variation is also presented confirming well-known results from literature and design manuals.
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Luley, Christopher J., and Harold S. McNabb Jr. "Estimation of seasonal ascospore production of Mycosphaerellapopulorum." Canadian Journal of Forest Research 21, no. 9 (September 1, 1991): 1349–53. http://dx.doi.org/10.1139/x91-190.
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A model was developed that estimates ascospore production by Mycosphaerellapopulorum Thompson in response to degree-days. Spore production and release in two field seasons were monitored with an ascospore liberation tunnel and Vaseline-coated slide traps, respectively. Ascospore production in a moist environment and under controlled temperatures was monitored in the laboratory with the liberation tunnel. Ascospores were produced in the laboratory for periods of 192, 98, and 74 days at 9, 16, and 21 °C, respectively, after initial ascospore maturation. Field and laboratory ascospore collection data were linearized with a Gompit transformation. Regression coefficients on the linearized data were greatest when a degree-day base of 0 °C was used. Slopes of the regression lines (k-values) developed from laboratory and field (1984 season) data were not significantly different. The k-values from laboratory data and data collected during the 1985 growing season were significantly different. The difference was attributed to dry weather during the period of ascospore production and release. The laboratory regression model could be used to predict ascospore production during the growing season after initial ascospore maturation if adequate moisture is present and if degree-day accumulation is predicted.
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Deogratias, Geradius, Makungu Madirisha, and Fortunatus Jacob. "Explicit Solutions for Real-time Reversible Inhibition Kinetics using Lambert W Function: Towards Progress Curve Analysis." Tanzania Journal of Science 48, no. 4 (December 30, 2022): 886–96. http://dx.doi.org/10.4314/tjs.v48i4.15.
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Accurate estimation of kinetic parameters is challenging due to the dynamics and mathematical nature of the chemical systems. This paper presents simple, yet efficient closed-form solutions for the enzymatic conversion of the substrate to the product in real-time derived using the Lambert W function. The real values of the Lambert W function were calculated from the Lambert package as implemented in MATLAB. The expressions exhibit remarkable robustness in estimating the parameters for randomized data at 1% to 4% variation in noise levels. Furthermore, unlike the initial rates method, the expressions estimate chemical kinetic parameters utilizing a full range of experimental data, thus minimizing the risk of missing information that would be detected at an extended time-span. Thus, the implementation of closed-form solutions presented in this paper for the estimation of kinetic parameters eliminates common pitfalls imposed by the initial rates and double reciprocal methods.
Keywords: Enzyme, Lambert W function, Reversible inhibition, Closed-form solutions
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Guo, Kai, Hu Ye, Honglin Chen, and Xin Gao. "A New Method for Absolute Pose Estimation with Unknown Focal Length and Radial Distortion." Sensors 22, no. 5 (February 25, 2022): 1841. http://dx.doi.org/10.3390/s22051841.
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Estimating the absolute pose of a camera is one of the key steps for computer vision. In some cases, especially when using a wide-angle or zoom lens, the focal length and radial distortion also need to be considered. Therefore, in this paper, an efficient and robust method for a single solution is proposed to estimate the absolute pose for a camera with unknown focal length and radial distortion, using three 2D–3D point correspondences and known camera position. The problem is decomposed into two sub-problems, which makes the estimation simpler and more efficient. The first sub-problem is to estimate the focal length and radial distortion. An important geometric characteristic of radial distortion, that the orientation of the 2D image point with respect to the center of distortion (i.e., principal point in this paper) under radial distortion is unchanged, is used to solve this sub-problem. The focal length and up to four-order radial distortion can be determined with this geometric characteristic, and it can be applied to multiple distortion models. The values with no radial distortion are used as the initial values, which are close to the global optimal solutions. Then, the sub-problem can be efficiently and accurately solved with the initial values. The second sub-problem is to determine the absolute pose with geometric linear constraints. After estimating the focal length and radial distortion, the undistorted image can be obtained, and then the absolute pose can be efficiently determined from the point correspondences and known camera position using the undistorted image. Experimental results indicate this method’s accuracy and numerical stability for pose estimation with unknown focal length and radial distortion in synthetic data and real images.
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Surulere, Samuel A., Micheal Y. Shatalov, Andrew C. P. G. Mkolesia, and Igor Fedotov. "A Modern Approach for the Identification of the Classical and Modified Generalized Morse Potential." Nanoscience & Nanotechnology-Asia 10, no. 2 (February 25, 2020): 142–51. http://dx.doi.org/10.2174/2210681208666181010141842.
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Introduction: This paper proposes an approach for parameter estimation of the Classical and Generalized Morse Potential functions. A new potential which is a modification of the Generalized Morse Potential was proposed as parameter estimates yielded complex conjugate roots using gold atom for simulation. Methods: Existing methods of parameter estimation requires the provision of initial guess values of which convergence to the optimal solution is not always guaranteed. This makes provision of initial guess values that guarantees convergence to the optimum solution more of an art than a science. The proposed objective least squares function method does not require the provision of initial guess values and it involves the minimization of two formulated objective functions using the differential numerical approach and least squares method. The built-in “Minimize” function of Mathematica® is also used to minimize the formulated objective function. Potential energy curves were constructed by fitting estimated parameter values to experimental data sets of the gold atom using values of the proposed approach and Mathematica® for performance evaluation. Errors of each constructed potential energy curves were simulated. Results: It was observed that the errors were very small for both the Classical and Modified Generalized Morse Potential. Conclusion: Hence the approximations of the proposed approach are very good.
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A.S.AL-SABBAH, Shrook, Ali Mithe HAFEDH, and Adham Mohammed SAHIB. "ESTIMATING FUZZY HAZARD RATE OF THREE PARAMETERS WEIBULL DISTRIBUTION." MINAR International Journal of Applied Sciences and Technology 02, no. 01 (March 1, 2020): 1–19. http://dx.doi.org/10.47832/2717-8234.1-2.1.
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It is noticed there are many researches and studies in the field of statistics particularly in the field of hazard aiming in order to obtain estimators of very high standard of competency or tainted by instances of uncertainty, which often leads the researcher to estimate the fuzzy hazard functions instead of the usual hazard function, it is the most comprehensive and representative of the data for which the hazard function is to be applied. This paper deals with estimating of fuzzy hazard rate function of three parameters Weibull, methods of Moments Estimators (MOE), maximum likelihood estimators (MLE), and Regression method. The comparison is done by simulation method using different sample size (n=40,60, 80) and initial values of ( (b: scale, c: shape : location parameter). after the parameters are estimated by different three methods (MLE, MOM, PEC(Regression) ; the values of ( ) is generated from C.D.F, using inverse transformation. set of five values of ( ) have been taken for application and estimation process. The goal of this process is to get the lowest value of the Mean square error, many fidelity criteria have been computed and the results have been discussed. Keywords: Fuzzy hazard rate, Weibull Distribution, Maximum Likelihood Estimator (MLE), Moments estimator (MOM), Percentiles.
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Kosten, Mehmet Muzaffer, Alper Emlek, Recep Yildiz, and Murat Barut. "Rotor Speed and Load Torque Estimations of Induction Motors via LSTM Network." Power Electronics and Drives 8, no. 1 (January 1, 2023): 310–24. http://dx.doi.org/10.2478/pead-2023-0021.
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Abstract In this study, a long short-term memory (LSTM) based estimator using rotating axis components of the stator voltages and currents as inputs is designed to perform estimations of rotor mechanical speed and load torque values of the induction motor (IM) for electrical vehicle (EV) applications. For this aim, first of all, an indirect vector controlled IM drive is implemented in simulation to collect both training and test datasets. After the initial training, a fine-tuning process is applied to increase the robustness of the proposed LSTM network. Furthermore, the LSTM parameters, layer size, and hidden size are also optimised to increase the estimation performance. The proposed LSTM network is tested under two different challenging scenarios including the operation of the IM with linear and step-like load torque changes in a single direction and in both directions. To force the proposed LSTM network, it is also tested under the variation of stator and rotor resistances for the both-direction scenario. The obtained results confirm the highly satisfactory estimation performance of the proposed LSTM network and its applicability for the EV applications of the IMs.
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Li, Cheng Mei, Jian Jun Wu, Xiao Ning Zhang, Xi Luan, and Hai Ge Xing. "Propagation Delay Difference Estimation for Initial Random Access in LTE Compatible Mobile Satellite Communications System." Advanced Materials Research 846-847 (November 2013): 647–50. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.647.
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In this paper, a two-step propagation delay difference estimation method is proposed for LTE compatible multi-beam satellite systems to ensure the initial random access (RA). For GEO satellite system, there exists a large propagation delay difference, we cannot directly apply the LTE delay estimation method. To deal with this issue, we first divide a satellite beam into some layered small sub-areas according to the different delay difference values. Then, two types of Physical Random Access Channel (PRACH) preamble burst format are given. The detailed PRACH parameters are provided. Finally, simulations are performed and the results verify the availability of our proposed design.
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Nader, Ingo W., Ulrich S. Tran, and Anton K. Formann. "Sensitivity to initial values in full non-parametric maximum-likelihood estimation of the two-parameter logistic model." British Journal of Mathematical and Statistical Psychology 64, no. 2 (April 15, 2011): 320–36. http://dx.doi.org/10.1348/000711010x531957.
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Zagrajek, Krzysztof. "A Survey Data Approach for Determining the Probability Values of Vehicle-to-Grid Service Provision." Energies 14, no. 21 (November 3, 2021): 7270. http://dx.doi.org/10.3390/en14217270.
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One of the key aspects of vehicle-to-grid technology (V2G) is the analysis of uncertainty in electric vehicle user behavior. Correct estimation of the amount of available energy from electric vehicles that are expected to provide ancillary services to the electricity system operator or to secure the end user’s demand is essential to design these services in an appropriate way. Therefore, it is necessary to analyze the probabilities of V2G service performance for different scenarios. This paper presents the author’s approach to determining the values of V2G service provision probabilities using survey data. It was found that estimating these values using simulation and forecasting tools makes sense when the coefficients resulting from survey responses are used as initial data. Thus, the paper also presents the results of the surveys that were conducted. As the results from the simulations show, the values of the probabilities of V2G services are not high, which should induce future operators of V2G services to offer a beneficial product for the customer.
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Chen, Yuhang, Hongjun Xiong, and Yi Guo. "DEKF-based SOC estimation study for lithium batteries." Journal of Physics: Conference Series 2263, no. 1 (April 1, 2022): 012020. http://dx.doi.org/10.1088/1742-6596/2263/1/012020.
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Abstract In view of the low estimation accuracy of single extended Kalman filter algorithm (EKF) and the influence of the initial value of state of charge (SOC), the double extended Kalman filter algorithm (DEKF) is used for online estimation of battery model parameters and SOC based on the establishment of second-order RC equivalent circuit model. A 36V 32A·h lithium iron phosphate battery pack is used as the research object, and experiments are designed for the actual battery conditions. The experimental results show that the DEKF algorithm has better convergence and adaptability under different conditions than a single EKF algorithm with inaccurate initial values of SOC, and the error is controlled within 5%, which proves the feasibility and effectiveness of the algorithm.