Search Results - (( variable estimation _ algorithm ) OR ( java application stemming algorithm ))
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1
Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique
Published 2015“…The fixed block matching uses the same block size throughout the motion estimation process while the variable block matching uses different block size. …”
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2
Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data
Published 2022“…The classical correlation estimators that employ the sample mean of the dependent and independent variables are known to be affected by outliers. …”
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3
Railway wheelset parameter estimation using signals from lateral velocity sensor
Published 2008“…The algorithm includes an instrumental variable (IV) element to reduce estimation bias and a variable forgetting factor for good parameter tracking and smooth steady state. …”
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Article -
4
Control of IC Engine: Design a Novel MIMO Fuzzy Backstepping Adaptive Based Fuzzy Estimator Variable Structure Control.
Published 2011“…This paper expands a Multi Input Multi Output (MIMO) fuzzy estimator variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. …”
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5
Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
Published 2022“…The suitable independent variables (hL, DBH, and CPA) were vital to estimating the dependent variable (Sc) and producing a carbon stock map for the final result. …”
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6
A hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation plant
Published 2023Subjects: “…Estimation…”
Conference paper -
7
Parameter estimation of a continuous-time plant – the least-absolute error with variable forgetting factor method
Published 2005“…The algorithm includes an instrumental variable (IV) element to reduce estimation bias and a variable forgetting factor for good parameter tracking and smooth steady state.…”
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8
Fast adaptive motion estimation search algorithm for H.264 encoder
Published 2012“…The latest H.264/AVC encoder adopted more advanced techniques such as multiple reference-frame motion estimation, 4 x 4 integers Discrete Cosine Transform (DCT), intra prediction, de-blocking filter, quarter pixel Motion Estimation (ME) with variable block size and novel entropy. …”
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9
Robust Estimation Methods And Outlier Detection In Mediation Models
Published 2010“…Mediation models refer to the relationships among three variables: an independent variables (IV), a potential mediating variable (M), and a dependent variable (DV). …”
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10
ADAPTIVE LINEAR ALGORITHMS FOR POWER SYSTEM FUNDAMENTAL AND HARMONIC ESTIMATION
Published 2015“…Hence, one of the objectives of this thesis is to address and enhance the introduced fundamental frequency adaptive filter method which was based on modified variable step size LMS (MVSS) algorithm using generalized square error normalized LMS algorithm. …”
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11
Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search
Published 2014“…Hybrid genetic algorithm (GA) and Nelder-Mead local search mode 1 has been used to minimize demand estimation errors. …”
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12
Model selection approaches of water quality index data
Published 2016“…Automatic model selection by using algorithm can avoid huge variability in model specification process compared to manual selection.With the employment of algorithm, the right model selected is then also used for forecasting purposes. …”
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13
Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)
Published 2021“…Hence, the integration of EM algorithm estimation is applicable in improving the performance of automated model selection procedures for multiple equations models…”
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Monograph -
14
Bayesian logistic regression model on risk factors of type 2 diabetes mellitus
Published 2016“…The coefficients of the variables mentioned above were estimated by the method of maximum likelihood and significant variables were further identified. …”
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15
Robust variable selection methods for large- scale data in the presence of multicollinearity, autocorrelated errors and outliers
Published 2016“…The Reweighted Multivariate Normal (RMVN) algorithm is incorporated in the algorithm of the BAE-Net. …”
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16
Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi
Published 2018“…Since these parameters are affected by different operation conditions and load changings, then an accurate estimation strategy is quite necessary. In this paper, stator resistance and disturbed load torque, which assumed to be variable, are estimated. …”
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Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River
Published 2025“…The hybrid model is a novel approach for estimating sediment load based on various input variables. …”
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18
Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali
Published 2017“…Observers are computational algorithms designed to estimate unmeasured state variables due to the lack of appropriate estimating devices or to replace the high-priced sensors in a plant. …”
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19
Artificial Robust Control of Robot Arm: Design a Novel SISO Backstepping Adaptive Lyapunov Based Variable Structure Control.
Published 2011“…The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. …”
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20
Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements
Published 2018“…The parameters investigated are apparent resistivity ( a ), horizontal location (x) and depth (z) as independent variable; while true resistivity ( t ) is dependent variable.…”
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