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1
Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…In principle, the optimization problem is regarded as a state estimation process. Each agent acts as a Kalman filter and finds solution to the optimization problem using a standard Kalman Filter framework which comprises of prediction, simulated measurement, and estimation phase that uses the best-so-far solution as a reference. …”
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2
Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems
Published 2011“…This enhancement produces the filtering solution by using the second sub-algorithm. In addition, an improvement is made in the third sub-algorithm where the minimum output residual is combined with the cost function. …”
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3
Explicit solution of parameter estimate using multiparametric programming for boost converter
Published 2023“…This work proposes an approach to estimate the parameters of capacitance and inductance in a boost converter using an explicit solution. …”
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4
Estimation-based Metaheuristics: A New Branch of Computational Intelligence
Published 2016“…Each agent in SKF acts as a Kalman Filter. By adapting the standard Kalman Filter framework, each individual agent finds an optimization solution by using a simulated measurement process that is guided by a best-so-far solution as a reference. …”
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5
Analysis of toothbrush rig parameter estimation using different model orders in Real-Coded Genetic Algorithm (RCGA)
Published 2018“…Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.…”
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6
The compact genetic algorithm for likelihood estimator of first order moving average model
Published 2012“…Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. …”
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7
Comparison between Newton’s Method and a new Scaling Newton Method / Ramizah Baharuddin
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8
Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real Coded Genetic Algorithm (RCGA)
Published 2024“…Generation gap used was 0.5 has shorten the algorithm conver-gence time without affecting the model accuracy.…”
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Liquid Flow Enhancement using Natural Polymeric Additives: Effect of Concentration
Published 2016“…An agent in the population finds solution to optimization problem using a standard Kalman Filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. …”
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10
A multiobjective simulated Kalman filter optimization algorithm
Published 2018“…A solution that has a lower cost function value and higher diversity spacing is considered as a solution that dominates other solutions and thus is ranked in a higher ranking. …”
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Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm
Published 2016“…An agent in the population finds solution to optimization problem using a standard Kalman Filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. …”
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12
Nonlinear adaptive algorithm for active noise control with loudspeaker nonlinearity
Published 2014“…The controller compensates the nonlinearity using a model of the saturation effect represented by Scaled Error Function (SEF). …”
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Estimation of photovoltaic models using an enhanced Henry gas solubility optimization algorithm with first-order adaptive damping Berndt-Hall-Hall-Hausman method
Published 2024“…The proposed EHGSO methodology based on the adaptive damping BHHH technique (EHGSOAdBHHH) is tested on Single Diode (SD), and Double Diode (DD) PV models using actual experimental data. EHGSOAdBHHH exhibits outstanding accordance with attained experimental data compared with other algorithms, and its superiority is validated using several statistical criteria.…”
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14
A Kalman-Filter-Based Sine-Cosine Algorithm
Published 2019“…A solution that has a lower cost function is considered as the best solution. …”
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Cubature kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems
Published 2023“…In control system, the CKF algorithm is used to estimate the true value of a hidden quantity from an observation signal that contain an uncertainty. …”
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Non-parametric maximum likelihood estimation of cure fraction for interval survival data.
Published 2011“…We implement the Turnbull algorithm for the survival function estimation using EM algorithm. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…The fitness function used is the correlation function in the SKF algorithm to optimize the cipher image produced using the Lorenz system. …”
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18
Indoor positioning using weighted magnetic field signal distance similarity measure and fuzzy based algorithms
Published 2021“…Firstly, the MF RPs database is reconstructed into a cluster database using the clustering algorithm. …”
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An efficient model for indoor radio signal prediction and coverage estimation / Md. Sumon Sarker
Published 2011“…It minimizes the number of transmitters in the corresponding indoor area using a novel integrated approach of the proposed ray-tracing and genetic algorithm (GA). …”
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20
Simulated kalman filter algorithm with improved accuracy
Published 2018“…A solution that has a lower cost function is consid-ered as the best solution. …”
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