Search Results - (( model estimation methods algorithm ) OR ( based regulation based algorithm ))
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Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization
Published 2018“…In previous works, biopolymerization carried out in scaled-up bioreactors is modeled through FFNN. No review discussed the role of different training algorithms in artificial neural network on the estimation of biopolymerization performance. …”
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Sensorless Adaptive Fuzzy Logic Control Of Permanent Magnet Synchronous Motor
Published 2008“…An estimation method based on the back EMF and flux estimation is presented to calculate the rotor position for medium to high speed. …”
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Thesis -
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Genetic algorithm for control and optimisation of exothermic batch process
Published 2013“…Therefore, genetic algorithm (GA) is proposed in this work since precise mathematical model is not required. …”
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Modelling and control strategies for hydrokinetic energy harnessing
Published 2020“…Subsequently, the metaheuristic method based on the combination of the Hill-Climbing Search (HCS) MPPT algorithm and the Fuzzy Logic Controller has been proposed to produce a variable step size compared to the fixed step size in conventional HCS algorithm. …”
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Development of Fluid Properties Correlation For Malaysian Crude
Published 2013“…This project will be used MATLAB software and Microsoft Excel through the method of Group Method of Data Handling (GMDH) . GMDH is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models. …”
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The transmission dynamic of the COVID 19 outbreak : a predictive dashboard
Published 2021“…The model is solved using a numerical algorithm of the Runge-Kutta method. …”
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Semiparametric inference procedure for the accelarated failure time model with interval-censored data
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. …”
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Multiple equations model selection algorithm with iterative estimation method
Published 2016“…In particular, an algorithm on model selection for seemingly unrelated regression equations model using iterative feasible generalized least squares estimation method is proposed. …”
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An Improved Scatter Search Algorithm for Parameter Estimation in Large-Scale Kinetic Models of Biochemical Systems
Published 2019“…Based on the results, the QOBLESS method can be used as an efficient parameter estimation method in large-scale kinetic model building.…”
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An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems
Published 2019“…Based on the results, the QOBLESS method can be used as an efficient parameter estimation method in large-scale kinetic model building.…”
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The transmission dynamic of the COVID 19 outbreak: A predictive dashboard
Published 2021“…The model is solved using a numerical algorithm of the Runge-Kutta method. …”
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Model selection approaches of water quality index data
Published 2016“…In order to select the best model, it is vital to ensure that proper estimation method is chosen in the modelling process.Different estimators have been proposed for the estimation of parameters of a model, including the least square and iterative estimators.This study aims to evaluate the forecasting performances of two algorithms on water quality index (WQI) of a river in Malaysia based on root mean square error (RMSE) and geometric root mean square error (GRMSE).Feasible generalised least squares (FGLS) and iterative maximum likelihood (ML) estimation methods are used in the algorithms, respectively.The results showed that SUREMLE-Autometrics has surpassed SURE-Autometrics; another simultaneous selection procedure of multipleequation models.Two individual selections, namely Autometrics-SUREMLE and Autometrics-SURE, though showed consistency only for GRMSE.All in all, ML estimation is a more appropriate method to be employed in this seemingly unrelated regression equations (SURE) model selection.…”
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Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm
Published 2018“…The performance of the algorithm is tested by using simulated data. The test results show that the algorithm can estimate the order and coefficients of the autoregressive model very well. …”
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PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah
Published 2017“…The parameter estimation step is concerned with the estimation of model parameters once the structure is known. …”
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An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli
Published 2020“…In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. …”
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The development of parameter estimation method for Chinese hamster ovary model using black widow optimization algorithm
Published 2020“…It will help the researcher get the fitted graph model, correct data, and estimate the value based on the data’s behaviour. …”
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The compact genetic algorithm for likelihood estimator of first order moving average model
Published 2012“…In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). …”
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Parameter estimation of tapioca starch hydrolysis process: Application of least squares and genetic algorithm
Published 2011Subjects: “…Gauss-Newton method…”
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Simulation algorithm of bayesian approach for choice-conjoint model
Published 2011“…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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