Results 201 to 210 of about 3,243,208 (249)
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Bounding the parameters of linear systems with input backlash
Proceedings of the 2005, American Control Conference, 2005., 2005In this note we present a two-stage procedure for deriving parameters bounds of linear systems with input backlash when the output measurement errors are bounded. First, using steady-state input-output data, parameters of the nonlinear dynamic block are tightly bounded.
CERONE, Vito, REGRUTO TOMALINO, Diego
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Input type and parameter resetting: Is naturalistic input necessary?
IRAL - International Review of Applied Linguistics in Language Teaching, 2007It has been argued that extended exposure to naturalistic input provides L2 learners with more of an opportunity to converge of target morphosyntactic competence as compared to classroom-only environments, given that the former provide more positive evidence of less salient linguistic properties than the latter (e.g., Isabelli 2004).
Rothman, Jason, Iverson, Michael
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Proceedings of the 14th annual conference companion on Genetic and evolutionary computation, 2012
Computer experiments are part of the daily business for many researchers within the area of computational intelligence. However, there is no standard for either human or computer readable documentation of computer experiments. Such a standard could considerably improve the collaboration between experimental researchers, given it is intuitive to use. In
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Computer experiments are part of the daily business for many researchers within the area of computational intelligence. However, there is no standard for either human or computer readable documentation of computer experiments. Such a standard could considerably improve the collaboration between experimental researchers, given it is intuitive to use. In
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Identification of Modal Parameters with Unknown Input
2003The techniques developed for structural identification are typically devoted to obtain a structural model on the base of information on the response and on the forcing action. In many situations, however, it can be necessary to refer only to the measured responses.
DE ANGELIS, Maurizio +2 more
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Calibrating Input Parameters via Eligibility Sets
2020 Winter Simulation Conference (WSC), 2020Reliable simulation analysis requires accurately calibrating input model parameters. While there has been a sizable literature on parameter calibration that utilizes directly observed data, much less attention has been paid to the situation where only output-level data are available to justify input parameter choices.
Yuanlu Bai, Henry Lam
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Uncertainty analysis in PSA with correlated input parameters
International Journal of Systems Assurance Engineering and Management, 2010The probability of occurrence of top event of a fault tree in probabilistic safety assessment (PSA) is estimated from the probabilities of the basic events which constitute the fault tree. However the failure probabilities of basic events are subjected to statistical uncertainty.
Durga Rao Karanki +4 more
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Parameter identifiability for heat conduction with a boundary input
Mathematics and Computers in Simulation, 2009The identification of the piecewise thermal conductivity from measurements of the temperature in both space and time is investigated. It is shown that the identification process provides not only a unique solution, but also that the identification is stable.
Semion Gutman, Junhong Ha
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Parameter estimation for Euler equations with uncertain inputs
2015 54th IEEE Conference on Decision and Control (CDC), 2015The paper presents a new state estimation algorithm for 2D incompressible Euler equations with periodic boundary conditions and uncertain but bounded inputs and initial conditions. The algorithm converges (in L2-sense) to a least squares estimator given incomplete and noisy observations. The results are illustrated by numerical examples.
Sergiy Zhuk, Tigran T. Tchrakian
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Accounting for parameter uncertainty in simulation input modeling
Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304), 2002We formulate and evaluate a Bayesian approach to probabilistic input modeling for simulation experiments that accounts for the parameter and stochastic uncertainties inherent in most simulations and that yields valid predictive inferences about outputs of interest. We use prior information to construct prior distributions on the parameters of the input
Faker Zouaoui, James R. Wilson
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