Search Results - (( probable estimation methods algorithm ) OR ( a estimation methods algorithm ))

Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

“…The rank-based methods, estimating algorithms, and resampling techniques that are developed do not involve the difficulties of the existing estimating procedures. …”
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Simulation algorithm of bayesian approach for choice-conjoint model by Zulhanif

“…As we know, the Multinomial Probit Model (MPM) is a method which assumes that chosen observations are independent but according to researchers the MPM is rarely used due to computational difficulties in computing the maximum likelihood estimates (MLE) for estimate MPM parameters. …”
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The research on the signal source number estimation algorithm by Peizhi, Wang, Mohamed, Raihani, Mustapha, Norwati, Manshor, Noridayu

“…The experimental results show that with the increase of the SNR and the number of array elements, the correct estimation probability of the algorithm also increases correspondingly, which provides a reliable experimental basis and performance evaluation for the estimation. © 2024 Institute of Advanced Engineering and Science. …”
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Tree-based contrast subspace mining method by Florence Sia Fui Sze

“…All existing mining contrast subspace methods (i.e. CSMiner and CSMiner-BPR) use density-based likelihood contrast scoring function to estimate the likelihood of a query object to target class against other class in a subspace. …”
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Simulated Kalman Filter algorithms for solving optimization problems by Nor Hidayati, Abdul Aziz

“…These algorithms are inspired by the estimation capability of the well-known Kalman filter estimation method. …”
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Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model by Selva A.M., Yahaya M.S., Azis N., Ab Kadir M.Z.A., Jasni J., Yang Ghazali Y.Z.

Comparison between Newton’s Method and a new Scaling Newton Method / Ramizah Baharuddin by Baharuddin, Ramizah

“…Newton's Method also called the Newton-Raphson method is a recursive algorithm for approximating the root of a differentiable function. …”
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Outlier detection in circular regression model using minimum spanning tree method by Nur Faraidah, Muhammad Di, Siti Zanariah, Satari, Roslinazairimah, Zakaria

“…Therefore, this study aims to develop new algorithms that can detect outliers by using the minimum spanning tree method. …”
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Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation by Rahimizadeh, Hamid

“…The estimation of unknown PDF is a common problem and in this study Gaussian kernel function which is most widely used nonparametric density estimation method has been used for PDF calculation. …”
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Robust estimation methods for fixed effect panel data model having block-concentrated outliers by Abu Bakar @ Harun, Nor Mazlina

“…The Ordinary Least Squares (OLS) is the commonly used method to estimate the parameters of fixed effect panel data model. …”
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Neighbour-based on-demand routing algorithms for mobile ad hoc networks by Ejmaa, Ali Mohamed E.

“…Accordingly, a Novel Density-Aware Routing algorithm (NDAR) is proposed. …”
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Parameter estimation of K-distributed sea clutter based on fuzzy inference and Gustafson-Kessel clustering by Davari, Atefeh, Marhaban, Mohammad Hamiruce, Mohd Noor, Samsul Bahari, Karimadini, Mohammad, Karimoddini, Ali

“…This is achieved by a pre-estimation using fuzzy clustering that provides a prior knowledge and forms a rough model to be fine tuned using the least square method. …”
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Scale-invariant and adaptive-search template matching for monocular visual odometry in low-textured environment by Aqel, Mohammad O. A.

“…The most important task for any autonomous mobile vehicle is the reliable estimation of its position over time. Visual odometry (VO) is a localization technique that estimates the position of a robot using only the stream of images acquired from a camera. …”
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Estimation of transformers health index based on condition parameter factor and hidden Markov model by Mohd Selva, Amran, Yahaya, Muhammad Sharil, Azis, Norhafiz, Ab Kadir, Mohd Zainal Abidin, Jasni, Jasronita, Yang Ghazali, Young Zaidey

“…This paper presents a study to estimate future Health Index (HI) of transformer population based on Hidden Markov Model (HMM). …”
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Area estimation of peninsular Malaysia by Numerical methods / Siti Nuruljannah Mohamed Zeinnudin and Nurul Kamaliah Omar by Mohamed Zeinnudin, Siti Nuruljannah, Omar, Nurul Kamaliah

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Identification of hammerstain model using stochastic perturbation simultaneous approximation by Nurriyah, Mohd Noor

“…Besides that, this project analysed the efficient of the SPSA in identify nonlinear system in term of object function and error with different noise variance. A numerical example is given to illustrate that the SPSA based algorithms can give accurate parameter estimate of the Hammerstein models with high probability through detailed simulation.…”
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Particle swarm optimization for NARX structure selection application on DC motor model: article / Mohd I. Abdullah by Abdullah, Mohd I.

“…This paper presents the nonlinear identification of a DC motor using Binary Particle Swarm Optimization (BPSO) algorithm, as a model structure selection method, replacing the typical Orthogonal Least Squares (OLS) used in system identification. …”
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Robust Kernel Density Function Estimation by Dadkhah, Kourosh

“…The classical kernel density estimation technique is the commonly used method to estimate the density function. …”
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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari by Satari, Siti Zanariah

“…In this study, we propose an alternative method of constructing a confidence interval based from the distribution of the estimated value of error concentration parameter obtained from the Fisher information matrix. …”
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Improved robust estimator and clustering procedures for multivariate outliers detection by Sharifah Sakinah, Syed Abd Mutalib

“…Hence, the main objective of this study is to propose a robust estimator in order to develop an improved procedure for detecting outliers in multivariate data using robust clustering-based methods. …”
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