Search Results - (( parallel distribution control algorithm ) OR ( parameter simulation model algorithm ))
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DC-based PV-powered home energy system
Published 2017“…The controller algorithm requires also the variation range of the geographical weather parameters (irradiance and temperature) to specify the MPP which is equivalent to that operating voltage at minimum weather parameters. …”
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Thesis -
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Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems
Published 2014“…To implement the parallel algorithms a distributed parallel computing laboratory using easily available low cost computers is setup. …”
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Distributed generation with parallel connected inverter
Published 2009“…DSP was used to generate the THIPWM and the control algorithm for the converter. PID controller is applied on the full-bridge dc/dc converter side to maintain the ac voltage to the required level. …”
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Conference or Workshop Item -
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Parallel distributed computational microcontroller system for adaptive antenna downlink transmitter power optimization
Published 2023Subjects:Article -
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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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Distributed generation system using parallel inverters supplied by unstable DC source
Published 2009“…This study presents the analysis and design of distributed generation system. The system consists of a full-bridge DC-DC converter, two units of three-phase inverters connected in parallel and a controller. …”
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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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Final Year Project -
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WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm
Published 2023Conference paper -
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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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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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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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Tumor growth prediction using parallel computing: numerical solutions based on multi-dimensional partial differential equation (PDE)
Published 2010“…This study focuses on the implementation of parallel algorithm for the simulation of tumor growth using two dimensional Helmholtz’s wave equation on a distributed parallel computing system. …”
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Book -
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A simulation study of a parametric mixture model of three different distributions to analyze heterogeneous survival data
Published 2013“…In this paper a simulation study of a parametric mixture model of three different distributions is considered to model heterogeneous survival data.Some properties of the proposed parametric mixture of Exponential, Gamma and Weibull are investigated.The Expectation Maximization Algorithm (EM) is implemented to estimate the maximum likelihood estimators of three different postulated parametric mixture model parameters.The simulations are performed by simulating data sampled from a population of three component parametric mixture of three different distributions, and the simulations are repeated 10, 30, 50, 100 and 500 times to investigate the consistency and stability of the EM scheme.The EM Algorithm scheme developed is able to estimate the parameters of the mixture which are very close to the parameters of the postulated model.The repetitions of the simulation give parameters closer and closer to the postulated models, as the number of repetitions increases, with relatively small standard errors.…”
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Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor
Published 2017“…This research focuses on the parameter estimation, outlier detection and imputation of missing values in a linear functional relationship model (LFRM). …”
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Simultaneous Computation of Model Order and Parameter Estimation for System Identification Based on Gravitational Search Algorithm
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. …”
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Conference or Workshop Item -
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Enhancing reservoir simulation models with genetic algorithm optimized neural networks across diverse climatic zones / Saad Mawlood Saab
Published 2025“…The optimizer algorithm (i.e., GA) determines the optimal input variables and internal parameters in the prediction models. …”
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A Model for Evaluation of Cryptography Algorithm on UUM Portal
Published 2004“…The development of the simulation model consists of seven steps. The steps are problem definition, construct the simulation model, test and validate the model, design the simulation experiments, conduct the simulation experiments, evaluate the result and implement the result. …”
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