Search Results - (( global distributions _ algorithm ) OR ( parameter simulation study algorithm ))
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1
Some families of count distributions for modelling zero-inflation and dispersion / Low Yeh Ching
Published 2016“…For parameter estimation, the simulated annealing global optimization routine and an EM-algorithm type approach for maximum likelihood estimation are studied. …”
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2
Efficient task scheduling strategies using symbiotic organisms search algorithm for cloud computing environment
Published 2022“…Also, the results of the simulations on normal, left-half, right-half and uniform datasets showed the proposed scheduling approach obtained a better performance on degree of imbalance over the benchmarked OTB-CSO algorithm. …”
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3
Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks
Published 2024“…Traditional BP networks face challenges, including parameter determination and slow convergence, addressed through genetic algorithms' global search capabilities. …”
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SWAT and ANN model hydrological assessment using Malaysia soil data / Khairi Khalid
Published 2017“…SWAT-CUP, which links SUFI-2 algorithm to SWAT models, has been utilized in the study for the calibration of SWAT models. …”
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5
Nomadic people optimizer (NPO) for large-scale optimization problems
Published 2019“…The NPO is inspired by the lifestyle of the nomads. The proposed algorithm simulates the behavior of the nomads when they are searching for life sources (water or grazing fields). …”
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6
Enhancing location estimation accuracy by utilizing time difference of arrival techniques for wireless network
Published 2020“…Standard deviation formula act as a parameter for accuracy estimation. Simulation have been conducted to provide a better understanding of the accuracy of TDOA based geo- location systems. …”
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Parameter-Less Simulated Kalman Filter
Published 2017“…Further studies on the effect of P(0), Q and R values suggest that the SKF algorithm can be realized as a parameter-less algorithm. …”
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9
Optimization of turning parameters using genetic algorithm method
Published 2008“…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
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Undergraduates Project Papers -
10
A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application
Published 2011“…The optimal simulation parameters can be used for the real application. …”
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Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor
Published 2017“…This study begins by proposing a robust technique for estimating the slope parameter in LFRM. …”
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12
Estimation in spot welding parameters using genetic algorithm
Published 2007“…In this study, parameter of spot welding estimate using computer simulation. …”
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13
Optimization of milling parameters using ant colony optimization
Published 2008“…The simulation based on ACO algorithm are successful develop and the optimization of parameters values is to maximize the production rate is obtain from the simulation.…”
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Undergraduates Project Papers -
14
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 -
15
The Implementation of a Machine Learning-based Routing Algorithm in a Lab-Scale Testbed
Published 2024“…Thus, researchers are developing intelligent RAs, including machine learning (ML)-based algorithms to meet traffic Q oS r equirements. However, most current studies evaluate performance using simulations. …”
Conference Paper -
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Clock Distribution Network Building Algorithm For Multiple Ips In System On A Chip
Published 2017“…In this project, an algorithm is proposed and developed to build the global CDN that is used to distribute the clocks to all partitions in the SoC using the channels available between partitions. …”
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17
Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
Published 2025“…This study proposed an improved parameter estimation procedure for PEMFCs by using the GOOSE algorithm, which was inspired by the adaptive behaviours found in geese during their relaxing and foraging times. …”
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Hybrid Predictive- Neural Network Based On Proton Exchange Membrane Fuel Cell Maximum Power Point Tracking
Published 2023“…Due to its various benefits, such as being environmentally friendly, highly efficient, noiseless, and safe to run, fuel cell power generation technology is gaining popularity in the present global energy generation, distribution, and consumer demand scene. …”
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Final Year Project Report / IMRAD -
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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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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…For its fast convergence and for its efficient search procedure, the self-adaptation is proposed in the parameters of the proposed hybrid algorithm. The effectiveness of this algorithm is verified by applying it on the unconstrained and constrained test functions through a simulation study. …”
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