Search Results - weibull distributions ((((using algorithm) OR (mining algorithm))) OR (using algorithms))
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An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia
Published 2023“…Data mining; Genetic algorithms; Meteorology; Neural networks; Planning; Sustainable development; Weibull distribution; Climate forecasts; Measure-correlate-predict; Measurement instruments; Measurement sites; Meteorological data; Reanalysis; Weibull frequency; Wind measurement; Forecasting…”
Conference Paper -
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Estimation Of Weibull Parameters Using Simulated Annealing As Applied In Financial Data
Published 2023“…The finding reveals that the Weibull distribution is well-suited to describing the investment behaviour of the MPS based on the estimates via the SA algorithm. …”
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Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah
Published 2015“…Four traffic distribution which are normal, lognormal, Weibull and exponential distribution are fitted and derived. …”
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Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah
Published 2015“…Four traffic distribution which are normal, lognormal, Weibull and exponential distribution are fitted and derived. …”
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5
Competing risks for reliability analysis using Cox’s model
Published 2007“…Purpose – Cox’s model with Weibull distribution and Cox’s with exponential distribution are the most important models in reliability analysis. …”
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Peak time bandwidth control algorithms with fitted traffic model on broadband network YouTube video traffic: article / Aini Azmi
Published 2016“…Results presents traffic characterizations are identified based on two (2) parameters Cumulative Distribution Functions (CDF) traffic model. Maximum Likelihood Estimator (MLE) technique is used to fit the best distribution model. …”
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Peak time bandwidth control algorithms with fitted traffic model on broadband network YouTube video traffic / Aini Azmi
Published 2016“…Results presents traffic characterizations are identified based on two (2) parameters Cumulative Distribution Functions (CDF) traffic model. Maximum Likelihood Estimator (MLE) technique is used to fit the best distribution model. …”
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Adaptive policing and shaping algorithms on inbound traffic using generalized Pareto distribution / Nor Azura Ayop
Published 2016“…Log likelihood estimation technique is used to fit the best 2-parameter CDF compared to Weibull, Normal and Rician distribution model. …”
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10
Bayesian survival and hazard estimates for Weibull regression with censored data using modified Jeffreys prior
Published 2013“…For the Weibull model with right censoring and unknown shape, the full conditional distribution for the scale and shape parameters are obtained via Gibbs sampling and Metropolis-Hastings algorithm from which the survival function and hazard function are estimated. …”
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Variational Bayesian inference for exponentiated Weibull right censored survival data
Published 2023“…The exponential, Weibull, log-logistic and lognormal distributions represent the class of light and heavy-tailed distributions that are often used in modelling time-to-event data. …”
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Probabilistic evaluation of wind power generation
Published 2023“…The paper presents an algorithm developed for a random wind speed generator governed by the probability density function of Weibull distribution and evaluates the WTG's output by using the power curve of wind turbines. …”
Conference paper -
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Statistical modeling of the electromechanical behaviour of BI-2223 composite wires
Published 2024text::Final Year Project -
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Multiobjective optimization using particle swarm optimization with non-Gaussian random generators
Published 2016“…The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. …”
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Multiobjective optimization using particle swarm optimization with non-Gaussian random generators
Published 2016“…The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. …”
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Multiobjective optimization using particle swarm optimization with non-Gaussian random generators
Published 2016“…The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. …”
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Multiobjective optimization using particle swarm optimization with non-Gaussian random generators
Published 2016“…The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. …”
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Mixture model of the Exponential, Gamma and Weibull distributions to analyse heterogeneous survival data
Published 2015“…Aims: In this study a survival mixture model of three components is considered to analyse survival data of heterogeneous nature.The survival mixture model is of the Exponential, Gamma and Weibull distributions.Methodology: The proposed model was investigated and the Maximum Likelihood (ML) estimators of the parameters of the model were evaluated by the application of the Expectation Maximization Algorithm (EM).Graphs, log likelihood (LL) and the Akaike Information Criterion (AIC) were used to compare the proposed model with the pure classical parametric survival models corresponding to each component using real survival data.The model was compared with the survival mixture models corresponding to each component.Results: The graphs, LL and AIC values showed that the proposed model fits the real data better than the pure classical survival models corresponding to each component.Also the proposed model fits the real data better than the survival mixture models corresponding to each component. …”
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Reliability Analysis and Prediction of Time to Failure Distribution of an Automobile Crankshaft
Published 2015“…The developed stochastic algorithm has the capability to measure the parametric distribution function and validate the predict the reliability rate, mean time to failure and hazard rate. …”
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Risk analysis of the copula dependent aggregate discounted claims with Weibull inter-arrival time
Published 2021“…We model the recursive moments of aggregate discounted claims, assuming the inter-claim arrival time follows a Weibull distribution to accommodate overdispersed and underdispersed data set. …”
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