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Bicomponent Mapping of Cortical Bone Using a New Interleaved UTE Imaging Sequence. [PDF]
Magn Reson MedShin SH +7 more
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Neuromorphic robust framework for integrated estimation and control in dynamical systems using spiking neural networks. [PDF]
Sci RepAhmadvand R, Sharif SS, Banad YM.
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Optimized Model Predictive Controller Using Multi-Objective Whale Optimization Algorithm for Urban Rail Train Tracking Control. [PDF]
Biomimetics (Basel)Wang L, Wang L, Chen Y.
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Enhanced self-potential inversion using a hybrid Second Horizontal Gradient and Bat Algorithm-SHGBA-framework for geothermal reservoir characterization. [PDF]
Sci RepEssa KS, Diab ZE, Gomaa OA, Elhussein M.
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Robust Simultaneous Estimation of Location Parameter
SSRN Electronic Journal, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qiang, Beidi, Peña, Edsel A.
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1988 American Control Conference, 1988
Motivated by uncertainty characterizations often appearing in robust control literature, we treat parameter identification in the presence of nonparametric weighted-ball-in-H? unmodelled dynamics, as well as induced-L2-norm-bounded unmodelled dynamics. A deterministic notion of information is presented and utilized. A robust parameter adjustment law is
James M. Krause, Pramod P. Khargonekar
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Motivated by uncertainty characterizations often appearing in robust control literature, we treat parameter identification in the presence of nonparametric weighted-ball-in-H? unmodelled dynamics, as well as induced-L2-norm-bounded unmodelled dynamics. A deterministic notion of information is presented and utilized. A robust parameter adjustment law is
James M. Krause, Pramod P. Khargonekar
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1998
In the last two chapters, we have designed and analyzed on-line parameter estimators and adaptive control schemes under the assumption that there are no modelling errors. Such an assumption is unrealistic since in the real world, modelling errors such as disturbances, sensor noise, unmodelled dynamics, nonlinearities, etc.
I. D. Landau, R. Lozano, M. M’Saad
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In the last two chapters, we have designed and analyzed on-line parameter estimators and adaptive control schemes under the assumption that there are no modelling errors. Such an assumption is unrealistic since in the real world, modelling errors such as disturbances, sensor noise, unmodelled dynamics, nonlinearities, etc.
I. D. Landau, R. Lozano, M. M’Saad
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Robust ascertainment‐adjusted parameter estimation
Genetic Epidemiology, 2005AbstractNonrandom ascertainment is commonly used in genetic studies of rare diseases, since this design is often more convenient than the random‐sampling design. When there is an underlying latent heterogeneity, Epstein et al. ([2002] Am. J. Hum. Genet.
Maengseok, Noh +2 more
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Nonregular Robust Parameter Designs
Statistics in Biopharmaceutical Research, 2009Robust parameter design, originally proposed by Taguchi (1986), has received considerable attention in recent years. The primary objective of a robust parameter design is to identify the setting of control factors of a product/system such that the mean of the response is optimal and the variance of the response is minimized.
Yingfu Li, Jiantian Wang, M. L. Aggarwal
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