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Parameterized Model Order Reduction
2015This Chapter introduces parameterized, or parametric, Model Order Reduction (pMOR). The Sections are offered in a prefered order for reading, but can be read independently. Section 5.1, written by Jorge Fernández Villena, L. Miguel Silveira, Wil H.A. Schilders, Gabriela Ciuprina, Daniel Ioan and Sebastian Kula, overviews the basic principles for pMOR ...
Ciuprina, Gabriela +11 more
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Proceedings of the 1997 American Control Conference (Cat. No.97CH36041), 1997
This paper concerns an input-order reduction problem. Control blending and disturbance direction identification are two examples of this class of problem. The approach is to maximize the Hankel norm of a reduced-input system so as to find a linear combination of control inputs that is most controllable and observable.
R.K. Douglas, R.H. Chen, J.L. Speyer
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This paper concerns an input-order reduction problem. Control blending and disturbance direction identification are two examples of this class of problem. The approach is to maximize the Hankel norm of a reduced-input system so as to find a linear combination of control inputs that is most controllable and observable.
R.K. Douglas, R.H. Chen, J.L. Speyer
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1986 25th IEEE Conference on Decision and Control, 1986
Three model reduction methods are described. These are the discrete balanced realizations of Mullis and Roberts [1],[2] where a characterization of the reduction error is given and a previously unknown L? norm bound on the reduction error, is obtained.
Ubaid M. Al-saggaf, Gene F. Franklin
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Three model reduction methods are described. These are the discrete balanced realizations of Mullis and Roberts [1],[2] where a characterization of the reduction error is given and a previously unknown L? norm bound on the reduction error, is obtained.
Ubaid M. Al-saggaf, Gene F. Franklin
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Modelling AIDS Reduction Strategies
International Journal of Epidemiology, 1995Mathematical models of the AIDS epidemic have not been able to give accurate predictions about the size of the epidemic because it is not possible to obtain sufficiently accurate measurements of the factors that enable HIV transmission. The uncertainties inherent in models of the AIDS epidemic appear to limit their relevance to epidemiologists. However,
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Using Kautz Models in Model Reduction
1998A method is presented for model reduction. It is based on the representation of the original model in an (exact) Kautz series. The Kautz series consists of orthogonalized exponential sequences. The Kautz series is non-unique: it depends on the ordering of the poles.
Brinker, den, A.C., Belt, H.J.W.
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2005
In this paper, the notions of polynomial–time model equivalent reduction and polynomial–space model equivalent reduction are introduced in order to investigate in a subtle way the expressive power of different theories. We compare according to these notions some classes of propositional formulas and quantified Boolean formulas.
Xishun Zhao, Hans Kleine Büning
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In this paper, the notions of polynomial–time model equivalent reduction and polynomial–space model equivalent reduction are introduced in order to investigate in a subtle way the expressive power of different theories. We compare according to these notions some classes of propositional formulas and quantified Boolean formulas.
Xishun Zhao, Hans Kleine Büning
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2015
A real system, especially a distributed parameter system, may havehigh or even infinite dimensions of freedom (DOF). When the DOF ofa model is too high, all inversion methods that we have learnedbecome inefficient and the inverse problem becomes unsolvablebecause of data and computational limitations.
Ne-Zheng Sun, Alexander Sun
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A real system, especially a distributed parameter system, may havehigh or even infinite dimensions of freedom (DOF). When the DOF ofa model is too high, all inversion methods that we have learnedbecome inefficient and the inverse problem becomes unsolvablebecause of data and computational limitations.
Ne-Zheng Sun, Alexander Sun
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The 23rd IEEE Conference on Decision and Control, 1984
In this paper, we consider model reduction by structured aggregation, where each reduced order state component is constrained to be simply a sum of components of the original state. This requirement, designed to preserve the physical significance of the system variables, is commonly imposed on models of large power systems, chemical process systems ...
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In this paper, we consider model reduction by structured aggregation, where each reduced order state component is constrained to be simply a sum of components of the original state. This requirement, designed to preserve the physical significance of the system variables, is commonly imposed on models of large power systems, chemical process systems ...
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