Search Results - (( parameter simulation model algorithm ) OR ( pattern estimation using algorithm ))
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1
Hybrid optimization approach to estimate random demand
Published 2012“…The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in local optima problems.The performance of the hybrid algorithm was compared with a single algorithm model in estimating parameter values that minimize objective function which was used to measure the goodness-of-fit between the observed data and simulated results.However, two problems had to be overcome in the forecasting random demand model. …”
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2
Fraud detection in telecommunication using pattern recognition method / Mohd Izhan Mohd Yusoff
Published 2014“…The new algorithm is tested on simulated and real data where the results show it is capable of detecting fraud activities. …”
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3
Determining the order of a moving average model of time series using reversible jump MCMC: a comparison between laplacian and gaussian noises
Published 2020“…After it has worked properly, it was applied to model human heart rate data. The results showed that the MCMC algorithm can estimate the parameters of the MA model. …”
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Epidemiological parameter estimation of sird model for covid-19 outbreak
Published 2022“…This paper is devoted to the parameter estimation of the SIRD model using the Markov Chain Monte Carlo (MCMC) method of the Metropolis Hasting algorithm. …”
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5
A Hydrologic Model for Studying the Climate Change Impact on Evapotranspiration and Water Yield in a Humid Tropical Watershed
Published 1998“…A distributed parameter modelling approach was used whereby a watershed was subdivided into relatively homogeneous ground response units (GRUs) to provide distributed parameter capabilities. …”
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6
Simulation of COVID-19 outbreaks via graphical user interface (GUI)
Published 2021“…An improved SIRD model was solved via the 4th order Runge-Kutta (RK4) method and 14 unknown parameters were estimated by using Nelder- Mead algorithm and pattern-search technique. …”
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Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis
Published 2004“…The Expectation Maximization (EM) algorithm is utilized to obtain the estimate of the parameters in the models. …”
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8
Modelling and Forecasting the Kuala Lumpur Composite Index Rate of Returns Using Generalised Autoregressive Conditional Heteroscedasticity Models
Published 2004“…Methods for correcting the outliers and splitting the heterogeneous data are proposed. The EM algorithm is applied to split the heterogeneous data, and the estimated parameters are used to correct the outlying data using the Mahalanobis Distance. …”
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9
Hybrid particle swarm optimization for robust digital image watermarking
Published 2011“…Moreover, the work takes accomplishing maximum robustness and transparency into consideration. HPSO method is used to estimate the multiple parameters involved in the model. …”
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Geometrical and dimensional defect evaluation of cold forged AA6061 propeller blade
Published 2013“…Comparison between the simulation result and the fabricated pin head show a geometrically similar pattern. …”
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11
Enhancing time series prediction with Hybrid AFSA-TCN: A unified approach to temporal data and optimization
Published 2025“…The dataset used in this study is a battery RUL dataset retrieved from an open-source platform Kaggle, which consists of more than 15,000 rows of time series data. …”
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Chemometric approaches in the evaluation of trace metals in commercially raised tilapia and preliminary health risk assessment of its consumption / Low Kah Hin
Published 2012“…For safety evaluation, the metal concentrations in the edible muscles were compared with the established legal limits and reasonable maximum exposures were simulated using the Monte Carlo algorithm.…”
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13
Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman
Published 2019“…Due to the center biased nature of the videos, the HPSO algorithm uses an initial pattern (hexagon-shaped) to speed up the convergence of the algorithm. …”
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14
Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway
Published 2023“…Fitting the simulated model into the experimental data is categorized under the parameter estimation problem. …”
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GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE
Published 2020“…The behavior of Genetic Algorithm (GA) where it generates and evolves the parameters towards a high-quality solution gives an advantage in obtaining ideal combination of parameters to fit in with the simulation. …”
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17
Estimation in spot welding parameters using genetic algorithm
Published 2007“…The application has widespread in many areas especially in system and control engineering. Genetic algorithm (GA) used as parameter estimation method for a model structure. …”
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18
Simultaneous computation of model order and parameter estimation for ARX model based on single swarm and multi swarm simulated Kalman filter
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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Simulation algorithm of bayesian approach for choice-conjoint model
Published 2011“…Therefore this research propose simulation algorithm of Bayesian approach for estimating parameter in MPM by Bayesian analysis to avoid computational difficulties in computing the maximum likelihood estimates (MLE).…”
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20
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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