Search Results - (( a distribution factor algorithm ) OR ( parameter adaptation model algorithm ))
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Fair bandwidth distribution marking and scheduling algorithm in network traffic classification
Published 2019“…Therefore, in order to improve bandwidth fairness along with efficient optimization to alleviate this problem, we propose two marker algorithms; a Double Modified Double Improved time sliding window Three Colour Marker (M2I2TSWTCM) algorithm, which makes a new value of that depends on the logarithm peak information rate (PIR) and which is added to the adaptive factor that exists in the previous algorithm. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…In order to estimate the quality of ‘pruned’ features, self-adaptive DE algorithm is proposed. Two parameters (population size and generation numbers) are adaptively adopted from number of remaining ranking features. …”
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3
Distributed joint power control, beamforming and spectrum leasing for cognitive two-way relay networks
Published 2017“…In the first part of the thesis, distributed power control and beamforming algorithm is proposed in which users operating in the underlay mode can strategically adapt their power levels and maximize their own utilities. …”
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Efficient task scheduling strategies using symbiotic organisms search algorithm for cloud computing environment
Published 2022“…However, the efficiency of the clouds drops as the size of the search space gets larger, like in the case of most metaheuristic optimisation algorithms. Furthermore, existing tasks scheduling solutions suffer from local optima entrapment due to inadequate diversification of their local search space, a high degree of imbalance because of their static control parameters cannot maintain a balance between local and global search space and scalability issue as a result of static benefit factors. …”
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FSM-Gear weight-based energy-efficient protocol for illegal logging monitoring using firefly synchronization
Published 2022“…The research objective is to have a low-power consumption protocol applied in a model network based on the actual environment suitable for illegal logging characteristics. …”
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7
Model-based hybrid variational level set method applied to object detection in grey scale images
Published 2024“…To robustly and accurately segment intricate object structures in challenging grayscale datasets, VKMHLS employs a multi-layer model-based level-set structure with adaptive scale operators. …”
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Novel parameter extraction for single, double, and three diodes photovoltaic models based on robust adaptive arithmetic optimization algorithm and adaptive damping method of Berndt-Hall-Hall-Hausman
Published 2022“…In this work, we present a robust adaptive Arithmetic Optimization Algorithm based on the adaptive damping Berndt-hall-hall-Hausman (RaAOAAdBHHH) approach to efficacity determine the parameters of the single, double, and three diode PV model. …”
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On the problem formulation for parameter extraction of the photovoltaic model: novel integration of hybrid evolutionary algorithm and Levenberg Marquardt based on adaptive damping parameter formula
Published 2022“…This paper presents an approach to determine the nine parameters of the three diode (TD) PV model based on the integration of the guaranteed convergence arithmetic optimization algorithm and Levenberg-Marquardt with adaptive damping nonlinear parameter method named as GCAOAAdLM. …”
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Debris flow susceptibility areas identification in Bukit Kachi, Sintok, Kedah using Flow R Software / Nursyamimi Che Daud
Published 2023“…Other than that, this study took into account worldwide susceptibility distribution from a macro perspective and chose factors or parameters based on the literature review of the previous study. …”
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Student Project -
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A comparative study for parameter selection in online auctions
Published 2009“…The bidding strategies applying self-adaptation model are expected to perform better than the deterministic dynamic adaptation because of the nature of the algorithm itself. …”
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Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model
Published 2021“…In this regard, a Local Sensitivity Analysis, Segment Particle Swarm Optimization (Se-PSO) algorithm, and the Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm was adapted and proposed to estimate the parameters. …”
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Decentralized Adaptive Pi With Adaptive Interaction Algorithm Of Wastewater Treatment Plant
Published 2014“…The Pi controller parameters are obtained by using simple updating algorithm developed based on adaptive interaction theory. …”
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LASSO-type estimations for threshold autoregressive and heteroscedastic time series models.
Published 2020“…We extend the application of the adaptive LASSO via the CGD algorithm for the multivariate Baba-Engle-Kroner-Kraft (BEKK) ARCH/GARCH, our fourth model. …”
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UMK Etheses -
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The effect of adaptive parameters on the performance of back propagation
Published 2012“…The activation functions are adjusted by the adaptation of gain parameters together with adaptive momentum and learning rate value during the learning process. …”
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A Novel Adaptive Spiral Dynamic Algorithm for Global Optimization
Published 2013“…This paper presents a novel adaptive spiral dynamic algorithm for global optimization. …”
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Conference or Workshop Item -
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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“…Then in terms of methodology, the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is combined with the Sine-Cosine mutualism phase of Symbiotic Organisms Search (SOS) for efficiently estimating the unknown parameters of PV models. …”
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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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