Search Results - (( parameter simulation based algorithm ) OR ( parameter estimation utilizing algorithm ))

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  1. 1
    Simultaneous computation of model order and parameter estimation for ARX model based on single swarm and multi swarm simulated Kalman filter

    Simultaneous computation of model order and parameter estimation for ARX model based on single swarm and multi swarm simulated Kalman filter by Kamil Zakwan, Mohd Azmi, Zuwairie, Ibrahim, Pebrianti, Dwi, Mohd Saberi, Mohamad

    Published 2017
    “…Simultaneous Model Order and Parameter Estimation (SMOPE) and Simultaneous Model Order and Parameter Estimation based on Multi Swarm (SMOPE-MS) are two techniques of implementing meta-heuristic algorithm to iteratively establish an optimal model order and parameters simultaneously for an unknown system. …”
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  2. 2
    LASSO-type estimations for threshold autoregressive and heteroscedastic time series models.

    LASSO-type estimations for threshold autoregressive and heteroscedastic time series models. by Muhammad Jaffri Mohd Nasir

    Published 2020
    “…In this thesis, we propose Least Absolute Shrinkage and Selection Operator (LASSO) type estimators to perform simultaneous parameter estimation and model selection for five specific univariate and multivariate time series models, and develop several algorithms to compute these estimators. …”
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  3. 3
    Augmented model based double iterative loop techniques for integrated system optimisation and parameter estimation of large scale industrial processes

    Augmented model based double iterative loop techniques for integrated system optimisation and parameter estimation of large scale industrial processes by Normah Abdullah, Brdys, M.A., Roberts, P.D.

    Published 1993
    “…The methods incorporate integrated system optimisation and parameter estimation which utilizes process measurements to achieve real process optimality inspite of model reality differences. …”
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  4. 4
    Enhanced location and positioning in wimax networks with virtual mimo base station

    Enhanced location and positioning in wimax networks with virtual mimo base station by Othman, Muhammad Hakim

    Published 2015
    “…The SMBS algorithm with virtual base station utilizes both AOA and AOD measurement parameter (SMVirBS). …”
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  5. 5
    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh

    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

    Published 2015
    “…The simulation results show the effectiveness of the ARDE method over other conventional techniques, transcending the limits of the existing state-of-the-art algorithms in estimating the parameters of robot. …”
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  6. 6
    A multiobjective simulated Kalman filter optimization algorithm

    A multiobjective simulated Kalman filter optimization algorithm by A. Azwan, A. Razak, Mohd Falfazli, Mat Jusof, Ahmad Nor Kasruddin, Nasir, Mohd Ashraf, Ahmad

    Published 2018
    “…It is a further enhancement of a single-objective Simulated Kalman Filter (SKF) optimization algorithm. …”
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  7. 7
    Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm

    Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

    Published 2025
    “…This study proposed an improved parameter estimation procedure for PEMFCs by using the GOOSE algorithm, which was inspired by the adaptive behaviours found in geese during their relaxing and foraging times. …”
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  8. 8
    Improved Location And Positioning Utilizing Single MIMO Base Station In IMT-Advanced System

    Improved Location And Positioning Utilizing Single MIMO Base Station In IMT-Advanced System by Azmi, Awang Md Isa, Mustafa H., Othman, Zahariah, Manap, Mohd Sa'ari, Mohamad Isa, Mohd Shahril Izuan, Mohd Zin, Mohd Shakir, Md Saat, Zahriladha, Zakaria

    Published 2016
    “…This algorithm based on the angle of arrival (AOA) and angle of departure (AOD) measurement parameter completed the new SMBS algorithm with virtual base station (SMVirBS). …”
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  9. 9
    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares

    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

    Published 2009
    “…They used the classical bootstrap method to estimate the bootstrap location and the scale parameters based on calculating the Mean of Squared Residual (MSR). …”
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  10. 10
    Active force control with iterative learning control algorithm for a vehicle suspension

    Active force control with iterative learning control algorithm for a vehicle suspension by Rosmazi, Rosli

    Published 2013
    “…The simulation was conducted using MATLAB/Simulink software package whilstthe experimental study utilized the existing experimental rig with a hardware-in-the-loop simulation (HILS) configuration with the proposed ILC algorithms incorporated as the new research contribution. …”
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  11. 11
    Online harmonic extraction and synchronization algorithm based control for unified power quality conditioner for microgrid systems

    Online harmonic extraction and synchronization algorithm based control for unified power quality conditioner for microgrid systems by Hasan, K., Othman, M.M., Meraj, S.T., Rahman, N.F.A., Noor, S.Z.M., Musirin, I., Abidin, I.Z.

    Published 2022
    “…Due to parallel computing feature and simple algorithm, ADALINE is utilized widely in parameter estimation. …”
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  12. 12
    Use of AR Block Processing for Estimating the State Variables of Power System

    Use of AR Block Processing for Estimating the State Variables of Power System by Mohd Nor, Nursyarizal, Jegatheesan, Ramiah, Perumal, Nallagownden

    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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  13. 13
    Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model

    Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model by Elfaki, Faiz. A. M.

    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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  14. 14
    Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network

    Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network by Dayang Suhaila, Awang Suhaili

    Published 2006
    “…The performances of the ANN were evaluated based on the coefficient of correlation, R. …”
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  15. 15
    Properties of selected garma models and their estimation procedures

    Properties of selected garma models and their estimation procedures by Ramiah Pillai, Thulasyammal

    Published 2012
    “…The focus of this study is to investigate the properties specically the variance and autocovariance of the GARMA (p; q; ±1; ±2) models. We also study the estimation of the parameters of these models. Evaluation of the performance of two estimators based on the Hannan-Rissanen Algorithm Estimator (HRA) and the Whittle's Estimator (WE) through a series of simulation studies have been conducted in this thesis. …”
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  16. 16
    Predicting the popularity of tweets using the theory of point processes.

    Predicting the popularity of tweets using the theory of point processes. by Tan, Wai Hong

    Published 2019
    “…The knowledge is combined using a novel empirical Bayes type approach, where the prior distribution for the model parameter is constructed based on the external knowledge, and the likelihood is calculated based on the internal knowledge. …”
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  17. 17
    Orthogonal Frequency Division Multiple Access resource unit allocation algorithms for IEEE 802.11AX medium access control protocol

    Orthogonal Frequency Division Multiple Access resource unit allocation algorithms for IEEE 802.11AX medium access control protocol by Zazali, Azyyati Adiah

    Published 2024
    “…The simulation results demonstrated that the proposed algorithms significantly outperformed the benchmark algorithms across several metrics. …”
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  18. 18
    Diagonalization of covariance matrix in simultaneous localization and mapping of mobile robot

    Diagonalization of covariance matrix in simultaneous localization and mapping of mobile robot by Maziatun, Mohamad Mazlan, Nur Aqilah, Othman, Hamzah, Ahmad

    Published 2019
    “…The purpose of this study is to analyze the behavior effects of covariance state update of different MATLAB simulation coding between modification algorithm of diaonal matrix using eigenvalue and using algorithm function of diagonalization directly, as it adds to computational cost of broadened Kalman channel based Synchronous Limitation and Mapping issue. …”
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  19. 19
    Enhancing location estimation accuracy by utilizing time difference of arrival techniques for wireless network

    Enhancing location estimation accuracy by utilizing time difference of arrival techniques for wireless network by Md Mahyuddin, Mohamad Fahmi

    Published 2020
    “…Standard deviation formula act as a parameter for accuracy estimation. Simulation have been conducted to provide a better understanding of the accuracy of TDOA based geo- location systems. …”
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  20. 20
    Adaptive impedance control for unknown non-flat environment

    Adaptive impedance control for unknown non-flat environment by Zainul Azlan, Norsinnira, Yamaura, Hiroshi

    Published 2013
    “…The force error feedback is utilized in the estimation and the accurate knowledge of the environment parameters are not required by the algorithm. …”
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