The Experts below are selected from a list of 27561 Experts worldwide ranked by ideXlab platform
Yuanguo Zhu - One of the best experts on this subject based on the ideXlab platform.
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The piecewise parametric optimal control of uncertain Linear Quadratic Models
International Journal of Systems Science, 2019Co-Authors: Yuanguo ZhuAbstract:ABSTRACTThe optimal control of Linear Quadratic model is given in a feedback form and determined by the solution of a Riccati equation. However, the control-related Riccati equation usually cannot ...
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A parametric optimization approach for uncertain Linear Quadratic Models
European Journal of Control, 2017Co-Authors: Yuanguo ZhuAbstract:Abstract As it is well known, the optimal control of Linear Quadratic model is given in a feedback form, which is determined by the solution of a Riccati equation. However, the corresponding Riccati equation cannot be solved analytically in many cases. Even if an analytic solution can be obtained, it might be a complex time-oriented function. In this paper, we introduce an approximate model with parameter for simplifying the form of optimal control of uncertain Linear Quadratic model. First, we discuss an optimal control problem of uncertain Linear Quadratic model and deduce an analytic expression of optimal control. Then we formulate an approximate model with parameter and present a parametric optimization method for solving the optimal parameter. Finally, a production planning problem is given to illustrate the efficiency of the proposed approximate model and parametric optimization approach.
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Parametric optimal control for uncertain Linear Quadratic Models
Applied Soft Computing, 2017Co-Authors: Yuanguo ZhuAbstract:Graphical abstractIn this paper, a parametric optimal control problem of uncertain Linear Quadratic model was proposed and investigated. Based on the equation of optimality, the optimal control and optimal value for the uncertain Linear Quadratic model without control parameter were given. Then we studied one kind of parametric optimal control problem of uncertain Linear Quadratic model and proposed a parametric approximation method for solving it. And the analytic expressions of optimal control and optimal value were derived. Finally, an application of parametric optimal control in the field of inventory-promotion problem was given to illustrate the efficiency of proposed method and to show the practicability of parametric optimal control model. Display Omitted HighlightsA multidimensional uncertain Linear Quadratic model with parameter is established.A parametric approximation method is proposed.Analytic expressions for optimal parameter and optimal control are derived.The efficiency of proposed method is shown by an application. In recent few decades, Linear Quadratic optimal control problems have achieved great improvements in theoretical and practical perspectives. For a Linear Quadratic optimal control problem, it is well known that the optimal feedback control is characterized by the solution of a Riccati differential equation, which cannot be solved exactly in many cases, and sometimes the optimal feedback control will be a complex time-oriented function. In this paper, we introduce a parametric optimal control problem of uncertain Linear Quadratic model and propose an approximation method to solve it for simplifying the expression of optimal control. A theorem is given to ensure the solvability of optimal parameter. Besides, the analytical expressions of optimal control and optimal value are derived by using the proposed approximation method. Finally, an inventory-promotion problem is dealt with to illustrate the efficiency of the results and the practicability of the model.
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Uncertain optimal control of Linear Quadratic Models with jump
Mathematical and Computer Modelling, 2012Co-Authors: Liubao Deng, Yuanguo ZhuAbstract:Abstract Based on the uncertain optimal control with jump, in this paper, we study a kind of special uncertain optimal control problem: Linear-Quadratic ( L Q ) uncertain optimal control problem with jump which has a Quadratic objective function for a Linear uncertain control system with jump. We obtain a necessary and sufficient condition for the existence of optimal control. As an application, we discuss an uncertain LQ optimal control problem for the enterprize’s investment decisions.
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Fuzzy optimal control of Linear Quadratic Models
Computers & Mathematics with Applications, 2010Co-Authors: Yanjuan Zhao, Yuanguo ZhuAbstract:Optimal control is a very important field of study not only in theory but in applications. Based on the concept of fuzzy process, a fuzzy optimal control model is investigated with a Quadratic objective functional for a Linear fuzzy control system.
Michael Vaeth - One of the best experts on this subject based on the ideXlab platform.
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The Shape of the Cancer Mortality Dose-Response Curve for the A-Bomb Survivors
Radiation Research, 1991Co-Authors: Donald A. Pierce, Michael VaethAbstract:The shape of the dose-response curve for cancer mortality in the A-bomb survivor data is analyzed in the context of Linear-Quadratic Models. Results are given for all cancers except leukemia as a group, for leukemia, and for combined inferences assuming common curvature. Since there is substantial information aside from these data suggesting a dose-response curve with upward curvature, the emphasis here is not on estimating the best-fitting dose-response curve, but rather on assessing the maximum curvature under Linear-Quadratic Models which is consistent with the data. The apparent shape of the dose-response curve is substantially affected by imprecision in the dose estimates, and methods are applied to correct for this. The extent of curvature can be expressed as the factor by which Linear risk estimates from these data should be divided to arrive at appropriate estimates of risk at low doses. Influential committees have in the past recommended ranges of 1.5-4 and of 2-10 for such a factor. Results here suggest that values greater than about 2.0-2.5 are at least moderately inconsistent with these data, within the context of Linear-Quadratic Models. It is emphasized, however, that there is little direct information in these data regarding risks following low doses; the inferencesmore » here depend strongly on the assumption of a Linear-Quadratic model.« less
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allowing for random errors in radiation dose estimates for the atomic bomb survivor data
Radiation Research, 1990Co-Authors: Donald A. Pierce, Daniel O Stram, Michael VaethAbstract:The presence of random errors in the individual radiation dose estimates for the A-bomb survivors causes underestimation of radiation effects in dose-response analyses, and also distorts the shape of dose-response curves. Statistical methods are presented which will adjust for these biases, provided that a valid statistical model for the dose estimation errors is used. Emphasis is on clarifying some rather subtle statistical issues. For most of this development the distinction between radiation dose and exposure is not critical. The proposed methods involve downward adjustment of dose estimates, but this does not imply that the dosimetry system is faulty. Rather, this is a part of the dose-response analysis required to remove biases in the risk estimates. The primary focus of this report is on Linear dose-response Models, but methods for Linear-Quadratic Models are also considered briefly. Some plausible Models for the dose estimation errors are considered, which have typical errors in a range of 30-40% of the true values, and sensitivity analysis of the resulting bias corrections is provided. It is found that for these error Models the resulting estimates of excess cancer risk based on Linear Models are about 6-17% greater than estimates that make no allowance for dose estimation errors. This increase in risk estimates is reduced to about 4-11% if, as has often been done recently, survivors with dose estimates above 4 Gy are eliminated from the analysis.
Wachira Boonyanet - One of the best experts on this subject based on the ideXlab platform.
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Linear–Quadratic term structure Models for negative euro area yields
Economics Letters, 2017Co-Authors: Marco Realdon, Wachira BoonyanetAbstract:Four factor Linear–Quadratic Models (LQTSM) fit negative Euro yields well, as short yields can be negative, but not the longest yields. LQTSM outperform four factor Quadratic Models that permit negative yields, which in turn outperform affine Gaussian Models.
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Linear Quadratic term structure Models for negative euro area yields
Economics Letters, 2017Co-Authors: Marco Realdon, Wachira BoonyanetAbstract:Four factor Linear–Quadratic Models (LQTSM) fit negative Euro yields well, as short yields can be negative, but not the longest yields. LQTSM outperform four factor Quadratic Models that permit negative yields, which in turn outperform affine Gaussian Models.
Donald A. Pierce - One of the best experts on this subject based on the ideXlab platform.
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The Shape of the Cancer Mortality Dose-Response Curve for the A-Bomb Survivors
Radiation Research, 1991Co-Authors: Donald A. Pierce, Michael VaethAbstract:The shape of the dose-response curve for cancer mortality in the A-bomb survivor data is analyzed in the context of Linear-Quadratic Models. Results are given for all cancers except leukemia as a group, for leukemia, and for combined inferences assuming common curvature. Since there is substantial information aside from these data suggesting a dose-response curve with upward curvature, the emphasis here is not on estimating the best-fitting dose-response curve, but rather on assessing the maximum curvature under Linear-Quadratic Models which is consistent with the data. The apparent shape of the dose-response curve is substantially affected by imprecision in the dose estimates, and methods are applied to correct for this. The extent of curvature can be expressed as the factor by which Linear risk estimates from these data should be divided to arrive at appropriate estimates of risk at low doses. Influential committees have in the past recommended ranges of 1.5-4 and of 2-10 for such a factor. Results here suggest that values greater than about 2.0-2.5 are at least moderately inconsistent with these data, within the context of Linear-Quadratic Models. It is emphasized, however, that there is little direct information in these data regarding risks following low doses; the inferencesmore » here depend strongly on the assumption of a Linear-Quadratic model.« less
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allowing for random errors in radiation dose estimates for the atomic bomb survivor data
Radiation Research, 1990Co-Authors: Donald A. Pierce, Daniel O Stram, Michael VaethAbstract:The presence of random errors in the individual radiation dose estimates for the A-bomb survivors causes underestimation of radiation effects in dose-response analyses, and also distorts the shape of dose-response curves. Statistical methods are presented which will adjust for these biases, provided that a valid statistical model for the dose estimation errors is used. Emphasis is on clarifying some rather subtle statistical issues. For most of this development the distinction between radiation dose and exposure is not critical. The proposed methods involve downward adjustment of dose estimates, but this does not imply that the dosimetry system is faulty. Rather, this is a part of the dose-response analysis required to remove biases in the risk estimates. The primary focus of this report is on Linear dose-response Models, but methods for Linear-Quadratic Models are also considered briefly. Some plausible Models for the dose estimation errors are considered, which have typical errors in a range of 30-40% of the true values, and sensitivity analysis of the resulting bias corrections is provided. It is found that for these error Models the resulting estimates of excess cancer risk based on Linear Models are about 6-17% greater than estimates that make no allowance for dose estimation errors. This increase in risk estimates is reduced to about 4-11% if, as has often been done recently, survivors with dose estimates above 4 Gy are eliminated from the analysis.
Marco Realdon - One of the best experts on this subject based on the ideXlab platform.
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Linear–Quadratic term structure Models for negative euro area yields
Economics Letters, 2017Co-Authors: Marco Realdon, Wachira BoonyanetAbstract:Four factor Linear–Quadratic Models (LQTSM) fit negative Euro yields well, as short yields can be negative, but not the longest yields. LQTSM outperform four factor Quadratic Models that permit negative yields, which in turn outperform affine Gaussian Models.
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Linear Quadratic term structure Models for negative euro area yields
Economics Letters, 2017Co-Authors: Marco Realdon, Wachira BoonyanetAbstract:Four factor Linear–Quadratic Models (LQTSM) fit negative Euro yields well, as short yields can be negative, but not the longest yields. LQTSM outperform four factor Quadratic Models that permit negative yields, which in turn outperform affine Gaussian Models.