Search Results - (( parametric estimation a algorithm ) OR ( parameter evaluation method algorithm ))*
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Non-Parametric and Parametric Estimations of Cure Fraction Using Right-and Interval-Censored Data
Published 2011“…Then, a series of simulation studies was conducted to evaluate the performance of the proposed estimation approaches. …”
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Analysis of the ECG signal using SVD-based parametric modelling technique
Published 2011“…Prony’s algorithm is first used to obtain initial estimates of the model, while the Gauss-Newton method is applied to solve the non-linear least-square optimisation problem. …”
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Proceeding Paper -
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Semiparametric binary model for clustered survival data
Published 2014“…A backfitting algorithm is used in the derivation of the estimating equation for the parametric and nonparametric components of a semiparametric binary covariate model. …”
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Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications
Published 2018“…The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. …”
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Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani
Published 2018“…To evaluate the performance of the Weibull parameters’ estimator methods, two sets of data are considered, one based on simulated data with different random variable size and the other based on actual data collected from a wind farm in Iran. …”
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Optimal model order selection for Transient Error Autoregressive Moving Average (TERA) MRI reconstruction method
Published 2008“…These criteria were evaluated on MRI data sets based on the method of Transient Error Reconstruction Algorithm (TERA). …”
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Parameter estimation of Kumaraswamy Burr type X models based on cure models with or without covariates
Published 2017“…In this thesis, we considered two methods via the classical maximum likelihood estimation (MLE) and the Bayes estimation using the Gibbs sampling (G-S) algorithm to estimate the parameters of BKBX, KBX and Beta-Weibull (BWB) models. …”
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Optimal model order selection for transient error autoregressive moving average (TERA) MRI reconstruction method
Published 2008“…These criteria were evaluated on MRI data sets based on the method of Transient Error Reconstruction Algorithm (TERA). …”
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An In-depth Study of Ankle-Foot Orthosis Dynamics Modeling: Leveraging Non-Parametric Approach Via Artificial Neural Networks
Published 2024“…Subsequently, the model structure was chosen, followed by parameter estimation through the selected algorithm. …”
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A parametric mixture model of three different distributions: An approach to analyse heterogeneous survival data
Published 2014“…A parametric mixture model of three different distributions is proposed to analyse heterogeneous survival data.The maximum likelihood estimators of the postulated parametric mixture model are estimated by applying an Expectation Maximization Algorithm (EM) scheme.The simulations are performed by generating data, sampled from a population of three component parametric mixture of three different distributions. …”
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A simulation study of a parametric mixture model of three different distributions to analyze heterogeneous survival data
Published 2013“…In this paper a simulation study of a parametric mixture model of three different distributions is considered to model heterogeneous survival data.Some properties of the proposed parametric mixture of Exponential, Gamma and Weibull are investigated.The Expectation Maximization Algorithm (EM) is implemented to estimate the maximum likelihood estimators of three different postulated parametric mixture model parameters.The simulations are performed by simulating data sampled from a population of three component parametric mixture of three different distributions, and the simulations are repeated 10, 30, 50, 100 and 500 times to investigate the consistency and stability of the EM scheme.The EM Algorithm scheme developed is able to estimate the parameters of the mixture which are very close to the parameters of the postulated model.The repetitions of the simulation give parameters closer and closer to the postulated models, as the number of repetitions increases, with relatively small standard errors.…”
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SYSTEM IDENTIFICATION AND PARAMETRIC ESTIMATION OF INFERENTIAL CORIOLIS
Published 2009“…When experiment was conducted, mass flowrate was measured using inferential coriolis and a commercial flowmeter from a manufacturer i.e., Micro Motion. To validate both methods, a load cell was used as the reference. Details evaluations of three pressure flow scenarios namely the single pressure flow, the continuous pressure flow, and the multi pressure flow for a CNG refueling system are presented. …”
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Parametric maximum likelihood estimation of cure fraction using interval-censored data
Published 2013“…This paper shows derivation of the estimation equations for the cure rate parameter followed by a simulation study.…”
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An intelligent framework for modelling and active vibration control of flexible structures
Published 2004“…The first controller design is formulated so as to allow on-line modeling, controller design and implementation and thus, yield a self-tuning control algorithm. Performance of the AVC algorithm is assessed based on parametric design techniques, using RLS and GAS, and non-parametric design techniques, using MLP-NN and ANFIS in the suppression of vibration of the flexible structures. …”
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Modelling and Control of Ankle Foot Orthosis (AFO) for Children Utilising Soft Computing Towards Intelligent Approach
Published 2024“…In comparison, it can be concluded that MLP NN provided a more accurate estimation of the AFO dynamic model. …”
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System Identification of XY Table ballscrew drive using parametric and non parametric frequency domain estimation via deterministic approach
Published 2012“…The system for this case is XY milling table ballscrew drive. Both parametric and nonparametric procedure. In addition, comparison of estimated model transfer function obtained via non-linear least square (NLLS) and Linear least square estimator algorithm were also being addressed. …”
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