The Experts below are selected from a list of 2166 Experts worldwide ranked by ideXlab platform
Pablo Rodriguez-ramirez - One of the best experts on this subject based on the ideXlab platform.
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VSS - Sliding mode filtering for Polynomial systems
2012 12th International Workshop on Variable Structure Systems, 2012Co-Authors: Michael Basin, Pablo Rodriguez-ramirezAbstract:This paper addresses the mean-Square and mean-module filtering problems for a nonlinear Polynomial stochastic system with Gaussian white noises. The obtained solutions contain a sliding mode term, signum of the innovations process. It is shown that the designed sliding mode mean-Square filter generates the mean-Square estimate, which has the same minimum estimation error variance as the estimate given by the conventional mean-Square Polynomial filter [24], although the gain matrices of both filters are different. The designed sliding mode mean-module filter generates the mean-module estimate, which yields a better value of the mean-module criterion in comparison to the conventional mean-Square Polynomial filter. The theoretical result is complemented with an illustrative example verifying performance of the designed filters. It is demonstrated that the estimates produced by the designed sliding mode mean-Square filter and the conventional mean-Square Polynomial filter yield the same estimation error variance, and there is an advantage in favor of the designed sliding mode mean-module filter.
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Sliding mode filter design for nonlinear Polynomial systems with unmeasured states
Information Sciences, 2012Co-Authors: Michael Basin, Pablo Rodriguez-ramirezAbstract:This paper addresses the mean-Square and mean-module filtering problems for a nonlinear Polynomial stochastic system with Gaussian white noises. The obtained solutions contain a sliding mode term, signum of the innovations process. It is shown that the designed sliding mode mean-Square filter generates the mean-Square estimate, which has the same minimum estimation error variance as the estimate given by the conventional mean-Square Polynomial filter Basin et al. (2008) [8], although the gain matrices of both filters are different. The designed sliding mode mean-module filter generates the mean-module estimate, which yields a better value of the mean-module criterion in comparison to the conventional mean-Square Polynomial filter. The theoretical result is complemented with an illustrative example verifying performance of the designed filters. It is demonstrated that the estimates produced by the designed sliding mode mean-Square filter and the conventional mean-Square Polynomial filter yield the same estimation error variance, and there is an advantage in favor of the designed sliding mode mean-module filter.
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Sliding mode mean-module filter design for Polynomial systems
Proceedings of the 2011 American Control Conference, 2011Co-Authors: Michael Basin, Pablo Rodriguez-ramirezAbstract:This paper addresses the mean-module filtering problem for a stochastic Polynomial system with Gaussian white noises. The obtained solution contains a sliding mode term, signum of the innovations process. It is shown that the designed sliding mode filter generates the mean-module estimate, which yields a better value of the mean-module criterion in comparison to the mean-Square Polynomial filter. The theoretical result is complemented with an illustrative example verifying performance of the designed filter, which is compared to the mean-Square Polynomial filter. The simulation results confirm an advantage in favor of the designed sliding mode filter.
Michael Basin - One of the best experts on this subject based on the ideXlab platform.
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VSS - Sliding mode filtering for Polynomial systems
2012 12th International Workshop on Variable Structure Systems, 2012Co-Authors: Michael Basin, Pablo Rodriguez-ramirezAbstract:This paper addresses the mean-Square and mean-module filtering problems for a nonlinear Polynomial stochastic system with Gaussian white noises. The obtained solutions contain a sliding mode term, signum of the innovations process. It is shown that the designed sliding mode mean-Square filter generates the mean-Square estimate, which has the same minimum estimation error variance as the estimate given by the conventional mean-Square Polynomial filter [24], although the gain matrices of both filters are different. The designed sliding mode mean-module filter generates the mean-module estimate, which yields a better value of the mean-module criterion in comparison to the conventional mean-Square Polynomial filter. The theoretical result is complemented with an illustrative example verifying performance of the designed filters. It is demonstrated that the estimates produced by the designed sliding mode mean-Square filter and the conventional mean-Square Polynomial filter yield the same estimation error variance, and there is an advantage in favor of the designed sliding mode mean-module filter.
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Sliding mode filter design for nonlinear Polynomial systems with unmeasured states
Information Sciences, 2012Co-Authors: Michael Basin, Pablo Rodriguez-ramirezAbstract:This paper addresses the mean-Square and mean-module filtering problems for a nonlinear Polynomial stochastic system with Gaussian white noises. The obtained solutions contain a sliding mode term, signum of the innovations process. It is shown that the designed sliding mode mean-Square filter generates the mean-Square estimate, which has the same minimum estimation error variance as the estimate given by the conventional mean-Square Polynomial filter Basin et al. (2008) [8], although the gain matrices of both filters are different. The designed sliding mode mean-module filter generates the mean-module estimate, which yields a better value of the mean-module criterion in comparison to the conventional mean-Square Polynomial filter. The theoretical result is complemented with an illustrative example verifying performance of the designed filters. It is demonstrated that the estimates produced by the designed sliding mode mean-Square filter and the conventional mean-Square Polynomial filter yield the same estimation error variance, and there is an advantage in favor of the designed sliding mode mean-module filter.
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mean Square filter design for nonlinear Polynomial systems with poisson noise
American Control Conference, 2011Co-Authors: Michael Basin, Juan J MaldonadoAbstract:This paper presents the mean-Square filtering problem for incompletely measured Polynomial system states, confused with white Poisson noises, over linear observations. The problem is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering equations for any Polynomial system state with white Poisson noises over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular case of a third-order state equation. In the example, performance of the designed optimal filter is verified against the conventional mean-Square Polynomial filter designed for systems with white Gaussian noises.
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Sliding mode mean-module filter design for Polynomial systems
Proceedings of the 2011 American Control Conference, 2011Co-Authors: Michael Basin, Pablo Rodriguez-ramirezAbstract:This paper addresses the mean-module filtering problem for a stochastic Polynomial system with Gaussian white noises. The obtained solution contains a sliding mode term, signum of the innovations process. It is shown that the designed sliding mode filter generates the mean-module estimate, which yields a better value of the mean-module criterion in comparison to the mean-Square Polynomial filter. The theoretical result is complemented with an illustrative example verifying performance of the designed filter, which is compared to the mean-Square Polynomial filter. The simulation results confirm an advantage in favor of the designed sliding mode filter.
Xuerong Mao - One of the best experts on this subject based on the ideXlab platform.
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Mean Square Polynomial Stability of Numerical Solutions to a Class of
2014Co-Authors: Wei Liu, Mohammud Foondun, Xuerong MaoAbstract:The exponential stability of numerical methods to stochastic dierential equations (SDEs) has been widely studied. In contrast, there are relatively few works on Polynomial stability of numerical methods. In this letter, we address the question of reproducing the Polynomial decay of a class of SDEs using the Euler{Maruyama method and the backward Euler{Maruyama method. The key technical contribution is based on various estimates involving the gamma function.
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Mean Square Polynomial stability of numerical solutions to a class of stochastic differential equations
Statistics & Probability Letters, 2014Co-Authors: Wei Liu, Mohammud Foondun, Xuerong MaoAbstract:Abstract The exponential stability of numerical methods to stochastic differential equations (SDEs) has been widely studied. In contrast, there are relatively few works on Polynomial stability of numerical methods. In this letter, we address the question of reproducing the Polynomial decay of a class of SDEs using the Euler–Maruyama method and the backward Euler–Maruyama method. The key technical contribution is based on various estimates involving the gamma function.
Guy Bonnet - One of the best experts on this subject based on the ideXlab platform.
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Nonlocal/coarse graining homogenization of linear elastic media with non-separated scales using least-Square Polynomial filters
International Journal for Multiscale Computational Engineering, 2014Co-Authors: Julien Yvonnet, Guy BonnetAbstract:In this paper, a nonlocal computational method is proposed to construct a mesoscopic (coars-grained) model of linear elastic heterogeneous materials in the case of nonseparated scales. The framework, introduced in our previous paper (Yvonnet and Bonnet, 2014) extends the classical homogenization framework by using low-pass filters operators instead of averaging operators, and Green's nonlocal functions instead of localization operators. In the present work, we introduce a filtering procedure based on least-Square Polynomial approximation to avoid the numerical drawbacks of Gaussian filters in finite domains. The complete associated homogenization scheme is described, as well aa a numerical procedure based on finite elements to compute the different homogenized operators from a unit cell. The methodology is validated by analyzing both local and mesoscopic mechanical fields in structures where heterogeneities are of comparable size with respect to the loading characteristic fluctuation wavelength.
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nonlocal coarse graining homogenization of linear elastic media with non separated scales using least Square Polynomial filters
International Journal for Multiscale Computational Engineering, 2014Co-Authors: Julien Yvonnet, Guy BonnetAbstract:In this paper, a nonlocal computational method is proposed to construct a mesoscopic (coars-grained) model of linear elastic heterogeneous materials in the case of nonseparated scales. The framework, introduced in our previous paper (Yvonnet and Bonnet, 2014) extends the classical homogenization framework by using low-pass filters operators instead of averaging operators, and Green's nonlocal functions instead of localization operators. In the present work, we introduce a filtering procedure based on least-Square Polynomial approximation to avoid the numerical drawbacks of Gaussian filters in finite domains. The complete associated homogenization scheme is described, as well aa a numerical procedure based on finite elements to compute the different homogenized operators from a unit cell. The methodology is validated by analyzing both local and mesoscopic mechanical fields in structures where heterogeneities are of comparable size with respect to the loading characteristic fluctuation wavelength.
Wei Liu - One of the best experts on this subject based on the ideXlab platform.
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Mean Square Polynomial Stability of Numerical Solutions to a Class of
2014Co-Authors: Wei Liu, Mohammud Foondun, Xuerong MaoAbstract:The exponential stability of numerical methods to stochastic dierential equations (SDEs) has been widely studied. In contrast, there are relatively few works on Polynomial stability of numerical methods. In this letter, we address the question of reproducing the Polynomial decay of a class of SDEs using the Euler{Maruyama method and the backward Euler{Maruyama method. The key technical contribution is based on various estimates involving the gamma function.
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Mean Square Polynomial stability of numerical solutions to a class of stochastic differential equations
Statistics & Probability Letters, 2014Co-Authors: Wei Liu, Mohammud Foondun, Xuerong MaoAbstract:Abstract The exponential stability of numerical methods to stochastic differential equations (SDEs) has been widely studied. In contrast, there are relatively few works on Polynomial stability of numerical methods. In this letter, we address the question of reproducing the Polynomial decay of a class of SDEs using the Euler–Maruyama method and the backward Euler–Maruyama method. The key technical contribution is based on various estimates involving the gamma function.