The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Kristofer Jennings - One of the best experts on this subject based on the ideXlab platform.
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confidence measure estimation in dynamical systems Model Input set selection
American Control Conference, 2004Co-Authors: P B Deignan, Galen B King, Peter H. Meckl, Kristofer JenningsAbstract:An information-theoretic Input selection method for dynamical system Modeling is presented that qualifies the rejection of irrelevant Inputs from a candidate Input set with an estimate of a measure of confidence given only finite data. To this end, we introduce a method of determining the spatial interval of dependency in the context of the Modeling problem for bootstrap mutual information estimates on dependent time-series. Additionally, details are presented for determining an optimal binning interval for histogram-based mutual information estimates.
J.l. Speyer - One of the best experts on this subject based on the ideXlab platform.
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Model Input reduction
Proceedings of the 1997 American Control Conference (Cat. No.97CH36041), 1997Co-Authors: R.k. Douglas, Robert H. Chen, J.l. SpeyerAbstract: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.
P B Deignan - One of the best experts on this subject based on the ideXlab platform.
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confidence measure estimation in dynamical systems Model Input set selection
American Control Conference, 2004Co-Authors: P B Deignan, Galen B King, Peter H. Meckl, Kristofer JenningsAbstract:An information-theoretic Input selection method for dynamical system Modeling is presented that qualifies the rejection of irrelevant Inputs from a candidate Input set with an estimate of a measure of confidence given only finite data. To this end, we introduce a method of determining the spatial interval of dependency in the context of the Modeling problem for bootstrap mutual information estimates on dependent time-series. Additionally, details are presented for determining an optimal binning interval for histogram-based mutual information estimates.
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Efficient information-theoretic Model Input selection
The 2002 45th Midwest Symposium on Circuits and Systems 2002. MWSCAS-2002., 2002Co-Authors: P B Deignan, M.a. Franchek, Peter H. MecklAbstract:Of fundamental importance to proper system identification and virtual sensing is the determination and assessment of an optimal set of Input signals independent of the final Model form. If the system is causal and deterministic, it is possible to efficiently compute an information-theoretic optimal Input set for a desired uniform accuracy of the target estimate and maximal dimension of the candidate Input set. A branch and bound combinatorial optimization algorithm based on an estimate of joint mutual information is presented as part of a total coherent methodology of Input selection.
Claudio Garcia - One of the best experts on this subject based on the ideXlab platform.
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Detection of no-Model Input-output pairs in closed-loop systems
ISA transactions, 2017Co-Authors: Alain Segundo Potts, Christiam Segundo Morales Alvarado, Claudio GarciaAbstract:Abstract The detection of no-Model Input-output (IO) pairs is important because it can speed up the multivariable system identification process, since all the pairs with null transfer functions are previously discarded and it can also improve the identified Model quality, thus improving the performance of Model based controllers. In the available literature, the methods focus just on the open-loop case, since in this case there is not the effect of the controller forcing the main diagonal in the transfer matrix to one and all the other terms to zero. In this paper, a modification of a previous method able to detect no-Model IO pairs in open-loop systems is presented, but adapted to perform this duty in closed-loop systems. Tests are performed by using the traditional methods and the proposed one to show its effectiveness.
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detection of no Model Input output combination in transfer matrix in closed loop mimo systems
IFAC Proceedings Volumes, 2012Co-Authors: Osmel Reyes Vaillant, Rodrigo Juliani Correa De Godoy, Claudio GarciaAbstract:Abstract A method to detect Input/output (IO) combinations with no-Model or poor Model in the transfer matrix of a closed-loop MIMO system is proposed. Traditional approaches to IO selection are not adequate when used to detect no-Model IO combination of a closed-loop identification process. The feedback effect, controller action and the characteristics of the excitation signal employed during the pre-identification stage cause this limitation. In this proposal the detection of no-Model or poor Model IO combinations is made based on regularity of low values of polynomial coefficients of parametric identification Models. This information is gathered during the pre-identification stage. Improvement in Model estimation is obtained once these “null” combinations are zeroed, before the identification process takes place. A study case involving identification of a 2 x 2 MIMO system is discussed.
Andrew Briggs - One of the best experts on this subject based on the ideXlab platform.
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Cost-Effectiveness Uncertainty Analysis Methods: A Comparison of One-Way Sensitivity, Analysis of Covariance, and Expected Value of Partial Perfect Information.
Medical Decision Making, 2014Co-Authors: Jonathan D Campbell, R. Brett Mcqueen, Anne M. Libby, D. Eldon Spackman, J. J. Carlson, Andrew BriggsAbstract:Objectives. To compare Model Input influence on incremental net monetary benefit (INMB) across 3 uncertainty methods: 1) 1-way sensitivity analysis; 2) probabilistic analysis of covariance (ANCOVA)...
