Identification of model error bounds in l 1- and H ∞-norm

The Modeling of Uncertainty in Control Systems - Lecture Notes in Control and Information Sciences ◽

10.1007/bfb0036257 ◽

1994 ◽

pp. 139-155 ◽

Cited By ~ 1

Author(s):

Richard G. Hakvoort ◽

Paul M. J. Van den Hof

Keyword(s):

Error Bounds ◽

Model Error

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Nonparametric model error bounds for control design in the presence of nonlinear distortions

Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228) ◽

10.1109/cdc.2001.980733 ◽

2003 ◽

Author(s):

J. Schoukens ◽

R. Pintelon ◽

T. Dobrowiecki

Keyword(s):

Error Bounds ◽

Control Design ◽

Model Error ◽

Nonparametric Model ◽

Nonlinear Distortions

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A flexible algorithm for structuring and reducing model error bounds

Proceedings of 35th IEEE Conference on Decision and Control ◽

10.1109/cdc.1996.573568 ◽

2002 ◽

Author(s):

L.J.J.M. Ariaans ◽

A.a.H. Damen ◽

S. Weiland

Keyword(s):

Error Bounds ◽

Model Error

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Hard frequency-domain model error bounds from least-squares like identification techniques

IEEE Transactions on Automatic Control ◽

10.1109/9.148343 ◽

1992 ◽

Vol 37 (7) ◽

pp. 900-912 ◽

Cited By ~ 156

Author(s):

B. Wahlberg ◽

L. Ljung

Keyword(s):

Least Squares ◽

Frequency Domain ◽

Error Bounds ◽

Model Error ◽

Domain Model ◽

Identification Techniques

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The Preisach hysteresis model: Error bounds for numerical identification and inversion

Discrete & Continuous Dynamical Systems - S ◽

10.3934/dcdss.2013.6.101 ◽

2013 ◽

Vol 6 (1) ◽

pp. 101-119 ◽

Cited By ~ 3

Author(s):

Pavel Krejčí ◽

Keyword(s):

Error Bounds ◽

Model Error ◽

Hysteresis Model ◽

Preisach Hysteresis ◽

And Inversion

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Reduced model error analysis “application to synchronous machines"

Journal de Physique III ◽

10.1051/jp3:1994253 ◽

1994 ◽

Vol 4 (10) ◽

pp. 1999-2012 ◽

Cited By ~ 1

Author(s):

Nabil Derbel ◽

Mohamed B.A. Kamoun ◽

Michel Poloujadoff

Keyword(s):

Error Analysis ◽

Model Error ◽

Synchronous Machines ◽

Reduced Model ◽

Analysis Application

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ERROR BOUNDS FOR EXPECTATION VALUES OF ONE-ELECTRON MOLECULAR SYSTEMS

Le Journal de Physique Colloques ◽

10.1051/jphyscol:1970421 ◽

1970 ◽

Vol 31 (C4) ◽

pp. C4-123-C4-123

Author(s):

N. W. BAZLEY

Keyword(s):

Error Bounds ◽

Expectation Values ◽

Molecular Systems

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Bayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts

10.21236/ada568491 ◽

2012 ◽

Author(s):

Ralph F. Milliff ◽

Christopher K. Wikle ◽

L. M. Berliner ◽

Radu Herbei

Keyword(s):

Data Assimilation ◽

Hierarchical Model ◽

Bayesian Hierarchical Model ◽

Model Error ◽

Bayesian Hierarchical ◽

Ocean Data Assimilation

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Hierarchical Production Planning in a Stochastic Manufacturing System with Long-Run Average Cost: Asymptotic Optimality and Error Bounds

SSRN Electronic Journal ◽

10.2139/ssrn.1125453 ◽

1998 ◽

Cited By ~ 4

Author(s):

Suresh Sethi ◽

Hanqin Zhang ◽

Qing Zhang

Keyword(s):

Production Planning ◽

Error Bounds ◽

Average Cost ◽

Manufacturing System ◽

Asymptotic Optimality ◽

Hierarchical Production Planning ◽

Long Run

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Least Squares Monte Carlo and Approximate Linear Programming: Error Bounds and Energy Real Option Application

SSRN Electronic Journal ◽

10.2139/ssrn.3232687 ◽

2018 ◽

Cited By ~ 2

Author(s):

Selvaprabu Nadarajah ◽

Nicola Secomandi

Keyword(s):

Monte Carlo ◽

Linear Programming ◽

Least Squares ◽

Error Bounds ◽

Real Option ◽

Least Squares Monte Carlo ◽

Programming Error ◽

Approximate Linear Programming

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Universal approximation with error bounds for dynamic artificial neural network models: A tutorial and some new results

2011 IEEE International Symposium on Computer-Aided Control System Design (CACSD) ◽

10.1109/cacsd.2011.6044542 ◽

2011 ◽

Cited By ~ 4

Author(s):

Kwang Ki Kevin Kim ◽

Ernesto Rios Patron ◽

Richard D. Braatz

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Error Bounds ◽

Network Models ◽

Universal Approximation ◽

Neural Network Models ◽

Artificial Neural ◽

Artificial Neural Network Models

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