Search Results - (( square estimation methods algorithm ) OR ( parameter simulation based algorithm ))
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1
MULTIVARIABLE CLOSED-LOOP SYSTEM IDENTIFICATION USING ITERATIVE LEAKY LEAST MEAN SQUARES METHOD
Published 2017“…Channel isolation usmg partial correlation analysis and estimation of model parameters in the conventional multivariable closed-loop system identification approaches use the method of Least Square (LS) with the limitations, viz. …”
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2
Reduced-rank technique for joint channel estimation in TD-SCDMA systems.
Published 2013“…Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. …”
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3
Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
Published 2009“…The Ordinary Least Squares (OLS) method is often used to estimate the parameters of a linear model. …”
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4
Reduced rank technique for joint channel estimation and joint data detection in TD-SCDMA systems
Published 2013“…Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. …”
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5
Reduced Rank Technique for Joint Channel Estimation and Joint Data Detection in TD-SCDMA Systems
Published 2012“…Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. …”
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Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi
Published 2018“…Simulation results and their comparison with Particle Swarm Optimization based method show high performance and good ability of the proposed method in PMSM parameter estimation.…”
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7
Dynamic robust bootstrap method based on LTS estimators
Published 2009“…We call this method Dynamic Robust Bootstrap-LTS based (DRBLTS) because here we have employed the LTS estimator in the modified bootstrap algorithm. …”
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8
Railway wheelset parameter estimation using signals from lateral velocity sensor
Published 2008“…A type of parameter estimation technique based on the linear integral filter (LIF) method, the least-absolute error with variable forgetting factor (LAE+VFF) estimation method, is proposed in this paper to estimate the railway wheelset parameters modelled as a time-varying continuous-time (C-T) system. …”
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
Published 2025“…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. …”
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10
Enhanced location and positioning in wimax networks with virtual mimo base station
Published 2015“…Simulation results show that the proposed technique outperforms the linear least square (LLS) algorithm in terms of estimated location accuracy.…”
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11
Kalman filter based impedance parameter estimation for transmission line and distribution line
Published 2019“…Therefore, a detailed study on developing and evaluating the new algorithms for transmission line parameter estimation is considered in this thesis. …”
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12
Modified least trimmed squares method for face recognition / Nur Azimah Abdul Rahim
Published 2018“…It can be concluded that the modified algorithm decreases the biases, the variances and the mean squared errors of the LTS estimators. …”
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13
Missing value estimation methods for data in linear functional relationship model
Published 2017“…The performance of EM and EMB are measured using mean absolute error, root-mean-square error and estimated bias. The results of the simulation study suggested that both EM and EMB methods are applicable to the LFRM with EMB algorithm outperforms the standard EM algorithm. …”
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14
Use of AR Block Processing for Estimating the State Variables of Power System
Published 2008“…Samples of the local utility network for 103-bus parameter are collected and simulated using Burg and Modified Covariance algorithm to estimate the state variables. …”
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15
Sensorless Adaptive Fuzzy Logic Control Of Permanent Magnet Synchronous Motor
Published 2008“…Both experimental and simulation results obtained from the HMRASC and the position angle estimation algorithms showed superior results compared to other methods presented in the literature.…”
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16
A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…The research starts with developing the hybrid deep learning model consisting of DNN and a K-Means Clustering Algorithm. Then, the developed algorithm is implemented to estimate the parameters of the Lorenz system. …”
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17
Robust estimation methods for fixed effect panel data model having block-concentrated outliers
Published 2019“…The Ordinary Least Squares (OLS) is the commonly used method to estimate the parameters of fixed effect panel data model. …”
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18
Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources
Published 2021“…The detailed parametric analysis exhibits the competency of the proposed algorithm to explain the rheological features. Monte-Carlo simulation is performed by propagating uncertainty to investigate the dominant parameters affecting simulated results. …”
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Reduced rank technique for joint channel estimation and joint data detection in TD-SCDMA systems
Published 2013“…The Mean Square Error (MSE) and Bit Error Rate (BER) metrics analyzing the channel matrix precision among the methods is also suggested in this thesis for comparison;Least square (LS), post-processing, and the proposed reduced rank methods. …”
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20
Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model
Published 2000“…A generated data where the failure times were taken as exponentially distributed was used to further compare these two methods of estimation. From the simulation study for this particular case, we can conclude that the EM algorithm proved to be more superior in terms of mean value of parameter estimates, bias and root mean square error. …”
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