Search Results - (( java application a algorithm ) OR ( parameter estimation step algorithm ))

Search alternatives:

Refine Results
  1. 1
    Kinetic Parameter Estimation in Alkylation of Benzene with 1-Decene through Hybrid Particle Swarm Optimization

    Kinetic Parameter Estimation in Alkylation of Benzene with 1-Decene through Hybrid Particle Swarm Optimization by Mohd Zulkefle, Nurul Farihah

    Published 2012
    “…Parameter estimation was performed by varying the initial guess of PSO algorithm which will conduct global search of the parameters. …”
    Get full text
    Get full text
    Final Year Project
    Save to List
    Saved in:
  2. 2
    Semiparametric inference procedure for the accelarated failure time model with interval-censored data

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

    Published 2019
    “…A computationally simple two-step iterative algorithm, called estimationapproximation algorithm, is introduced for estimating the parameters of the model on the basis of the rank estimators. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  3. 3
    PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah

    PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah by Abdullah, Siti Muniroh

    Published 2017
    “…The parameter estimation step is concerned with the estimation of model parameters once the structure is known. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  4. 4
    Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway

    Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway by Ahmad Muhaimin, Ismail, Muhammad Akmal, Remli, Yee Wen, Choon, Nurul Athirah, Nasarudin, Norsyahidatul Nazirah, Ismail, Mohd Arfian, Ismail, Mohd Saberi, Mohamad

    Published 2023
    “…Most of the estimated kinetic parameter values obtained from the proposed algorithm are the closest to the experimental data.…”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  5. 5
    ADAPTIVE LINEAR ALGORITHMS FOR POWER SYSTEM FUNDAMENTAL AND HARMONIC ESTIMATION

    ADAPTIVE LINEAR ALGORITHMS FOR POWER SYSTEM FUNDAMENTAL AND HARMONIC ESTIMATION by MUBARAK MOHMMED, HUSSAM ALHAJ

    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. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  6. 6
    Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi

    Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi by Dejamkhooy, Abdolmajid, Asefi, Sajjad

    Published 2018
    “…In the next step, the parameter identification as an optimization problem is solved by Moth-flame optimization, which is a novel nature-inspired heuristic algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  7. 7
    Parameter estimation of stochastic differential equation

    Parameter estimation of stochastic differential equation by Haliza Abd. Rahman, Arifah Bahar, Norhayati Rosli, Madihah Md. Salleh

    Published 2012
    “…Regression spline with Bayesian approach is considered in the first step of a two-step method in estimating the structural parameters for stochastic differential equation (SDE). …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  8. 8
    Simultaneous Computation of Model Order and Parameter Estimation for System Identification Based on Gravitational Search Algorithm

    Simultaneous Computation of Model Order and Parameter Estimation for System Identification Based on Gravitational Search Algorithm by Kamil Zakwan, Mohd Azmi, Pebrianti, Dwi, Zuwairie, Ibrahim, Shahdan, Sudin, Sophan Wahyudi, Nawawi

    Published 2015
    “…In this paper, a technique termed as Simultaneous Model Order and Parameter Estimation (SMOPE), which is specifically based on Gravitational Search Algorithm (GSA) is proposed to combine model order selection and parameter estimation in one process. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  9. 9
    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
    “…The Expectation Maximization (EM) was considered to obtain the estimate of the parameters. These estimates were then compared to the Newton-Raphson iteration method. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  10. 10
    Analysis of transient multiexponential signals using exponential compensation deconvolution

    Analysis of transient multiexponential signals using exponential compensation deconvolution by Jibia, Abdussamad Umar, Salami, Momoh Jimoh Eyiomika

    Published 2012
    “…A three-step procedure for the parameter estimation of transient multiexponential signals is proposed. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  11. 11
    Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm

    Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm by Zuwairie, Ibrahim, Mohd Zaidi, Mohd Tumari, Asrul, Adam, Norrima, Mokhtar, Marizan, Mubin, Mohd Ibrahim, Shapiai

    Published 2014
    “…Using GSA, the parameter estimation of the classifier and the peak feature selection can be done simultaneously. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  12. 12
    SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS

    SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS by YAZID, EDWAR

    Published 2015
    “…The first step is presenting the backward estimator and combined forward-backward estimator instead of the only forward estimator in the original input-output models; the second step is reformulating the input-output models into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the model coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Bee Colony (ABC) to form the PSO-KS, GA-KS and ABC-KS as estimation methods.…”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  13. 13
    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence

    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence by S. S. S. , Ranjit

    Published 2011
    “…Block-matching algorithm is the most common technique applied in block-based motion estimation technique. …”
    Get full text
    Get full text
    Get full text
    Book Chapter
    Save to List
    Saved in:
  14. 14
    Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter

    Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter by ., Edwar Yazid, Mohd Shahir Liew, Setyamartana Parman, Velluruzhati

    Published 2015
    “…The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC). …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  15. 15
    Combining Recursive Least Square and Principal Component Analysis for Assisted History Matching

    Combining Recursive Least Square and Principal Component Analysis for Assisted History Matching by Md. Anuar, Nurul Syaza

    Published 2014
    “…Even though RLS is a simple and effective method to estimate parameters, RLS have stability problem when number of parameters is high. …”
    Get full text
    Get full text
    Final Year Project
    Save to List
    Saved in:
  16. 16
    A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter

    A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter by Ur Rehman, M.J., Dass, S.C., Asirvadam, V.S.

    Published 2018
    “…Within the parameter learning steps, the MCMC algorithm requires to perform state estimation for which the target distribution is constructed by using the Ensemble Kalman filter (EnKF). …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  17. 17
    Parameter-driven count time series models / Nawwal Ahmad Bukhari

    Parameter-driven count time series models / Nawwal Ahmad Bukhari by Nawwal , Ahmad Bukhari

    Published 2018
    “…Simulation shows that MCEM algorithm and particle method are useful for the parameter estimation of the Poisson model. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  18. 18
    Penalized LAD-SCAD estimator based on robust wrapped correlation screening method for high dimensional models

    Penalized LAD-SCAD estimator based on robust wrapped correlation screening method for high dimensional models by Baba, Ishaq Abdullahi, Midi, Habshah, Leong, Wah June, Ibragimov, Gafurjan I.

    Published 2021
    “…The SIS method uses the rank correlation screening (RCS) algorithm in the pre-screening step and the traditional Pathwise coordinate descent algorithm for computing the sequence of the regularization parameters in the post screening step for onward model selection. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  19. 19
    Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman smoother adaptive filter

    Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman smoother adaptive filter by Yazid, E., Liew, M.S., Parman, S., Kurian, V.J.

    Published 2015
    “…The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC). …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  20. 20
    The Newton-like properties of the updating mechanism of a model-reality differences algorithm

    The Newton-like properties of the updating mechanism of a model-reality differences algorithm by Ahmad, Rohanin, Abd. Aziz, Mohd. Ismail

    Published 2003
    “…The Dynamic Integrated Systems Optimization and Parameter Estimation (DISOPE) algorithm is an algorithm for solving nonlinear optimal control problems and is of the gradient descent type. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:

Search Tools: