Search results for "Model complexity"

showing 2 items of 2 documents

Measurement-based load modelling for power supply system design

2008

Load modelling is essential to simulate system features as closely as possible to the effective behaviour. In spite of model complexity, the need for accuracy often leads to a component-based approach, i.e. the analysis of load internal subsystems. It is a common belief that measurement-based load models lead to low accuracy. This paper presents a new, high-accuracy measurement-based load modelling approach to define a power consumption profile load model for power systems design. The load modeling technique is described by an application. Simulation and experimental results are compared. The efficiency and portability of the proposed modelling approach is discussed. ©2008 IEEE.

Engineeringbusiness.industryLoad modelingControl engineeringSettore ING-IND/32 - Convertitori Macchine E Azionamenti ElettriciSettore ING-INF/01 - ElettronicaModel complexityPower (physics)Electric power systemSoftware portabilityPower supply modelingPower consumptionComponent (UML)Systems designbusiness2008 11th Workshop on Control and Modeling for Power Electronics
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Selecting the tuning parameter in penalized Gaussian graphical models

2019

Penalized inference of Gaussian graphical models is a way to assess the conditional independence structure in multivariate problems. In this setting, the conditional independence structure, corresponding to a graph, is related to the choice of the tuning parameter, which determines the model complexity or degrees of freedom. There has been little research on the degrees of freedom for penalized Gaussian graphical models. In this paper, we propose an estimator of the degrees of freedom in $$\ell _1$$ -penalized Gaussian graphical models. Specifically, we derive an estimator inspired by the generalized information criterion and propose to use this estimator as the bias term for two informatio…

Statistics and ProbabilityStatistics::TheoryKullback–Leibler divergenceKullback-Leibler divergenceComputer scienceGaussianInformation Criteria010103 numerical & computational mathematicsModel complexityModel selection01 natural sciencesTheoretical Computer Science010104 statistics & probabilitysymbols.namesakeStatistics::Machine LearningGeneralized information criterionEntropy (information theory)Statistics::MethodologyGraphical model0101 mathematicsPenalized Likelihood Kullback-Leibler Divergence Model Complexity Model Selection Generalized Information Criterion.Model selectionEstimatorStatistics::ComputationComputational Theory and MathematicsConditional independencesymbolsPenalized likelihoodStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaAlgorithmStatistics and Computing
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