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Добірка наукової літератури з теми "Linear parameter"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Linear parameter"

1

Lindow, Norbert, Daniel Baum, and Hans-Christian Hege. "Perceptually Linear Parameter Variations." Computer Graphics Forum 31, no. 2pt3 (May 2012): 535–44. http://dx.doi.org/10.1111/j.1467-8659.2012.03054.x.

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2

Potocký, Rastislav, and Van Ban To. "On parameter-effects arrays in non-linear regression models." Applications of Mathematics 38, no. 2 (1993): 123–32. http://dx.doi.org/10.21136/am.1993.104539.

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3

Cieśliński, Jan L., and Dzianis Zhalukevich. "Spectral Parameter as a Group Parameter." Symmetry 14, no. 12 (December 6, 2022): 2577. http://dx.doi.org/10.3390/sym14122577.

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A large class of integrable non-linear partial differential equations is characterized by the existence of the associated linear problem (in the case of two independent variables, known as a Lax pair) containing the so-called spectral parameter. In this paper, we present and discuss the conjecture that the spectral parameter can be interpreted as the parameter of some one-parameter groups of transformation, provided that it cannot be removed by any gauge transformation. If a non-parametric linear problem for a non-linear system is known (e.g., the Gauss–Weingarten equations as a linear problem for the Gauss–Codazzi equations in the geometry of submanifolds), then, by comparing both symmetry groups, we can find or indicate the integrable cases. We consider both conventional Lie point symmetries and the so-called extended Lie point symmetries, which are necessary in some cases. This paper is intended to be a review, but some novel results are presented as well.
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4

Sah, Binod Kumar. "One-parameter linear-exponential distribution." International Journal of Statistics and Applied Mathematics 6, no. 6 (November 1, 2021): 06–15. http://dx.doi.org/10.22271/maths.2021.v6.i6a.744.

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5

Kallianpur, G., and R. S. Selukar. "Parameter estimation in linear filtering." Journal of Multivariate Analysis 39, no. 2 (November 1991): 284–304. http://dx.doi.org/10.1016/0047-259x(91)90102-8.

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6

Despotovic, Vladimir, Tomas Skovranek, and Zoran Peric. "One-parameter fractional linear prediction." Computers & Electrical Engineering 69 (July 2018): 158–70. http://dx.doi.org/10.1016/j.compeleceng.2018.05.020.

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7

Lin, Chien-Fu Jeff, and Timo Teräsvirta. "Testing parameter constancy in linear models against stochastic stationary parameters." Journal of Econometrics 90, no. 2 (June 1999): 193–213. http://dx.doi.org/10.1016/s0304-4076(98)00041-4.

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8

Mohammad, K. S., K. Worden, and G. R. Tomlinson. "Direct parameter estimation for linear and non-linear structures." Journal of Sound and Vibration 152, no. 3 (February 1992): 471–99. http://dx.doi.org/10.1016/0022-460x(92)90482-d.

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9

Sagnella, G. A. "Model fitting, parameter estimation, linear and non-linear regression." Trends in Biochemical Sciences 10, no. 3 (March 1985): 100–103. http://dx.doi.org/10.1016/0968-0004(85)90261-0.

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10

Wirth, Fabian. "Stability of Linear Parameter Varying and Linear Switching Systems." PAMM 3, no. 1 (December 2003): 144–47. http://dx.doi.org/10.1002/pamm.200310348.

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Дисертації з теми "Linear parameter"

1

Hernandez, Erika Lyn. "Parameter Estimation in Linear-Linear Segmented Regression." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3551.pdf.

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2

Ollikainen, Kati. "PARAMETER ESTIMATION IN LINEAR REGRESSION." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4138.

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Анотація:
Today increasing amounts of data are available for analysis purposes and often times for resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the usual 95% level for medium sized data sets. A controlled environment using simulation with known data characteristics (clean data, bias and or multicollinearity present) was used to show underlying problems exist with confidence intervals not including the true parameter (even though the variable was selected). The Elder/Pregibon rule was used for variable selection. A comparison of the bootstrap Percentile and BCa confidence interval was made as well as an investigation of adjustments to the usual 95% confidence intervals based on the Bonferroni and Scheffe multiple comparison principles. The results show that linear regression has problems in capturing the true parameters in the confidence intervals for the sample sizes considered, the bootstrap intervals perform no better than linear regression, and the Scheffe method is too wide for any application considered. The Bonferroni adjustment is recommended for larger sample sizes and when the t-value for a selected variable is about 3.35 or higher. For smaller sample sizes all methods show problems with type II errors resulting from confidence intervals being too wide.
Ph.D.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering and Management Systems
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3

Zhang, Xiping. "Parameter-Dependent Lyapunov Functions and Stability Analysis of Linear Parameter-Dependent Dynamical Systems." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/5270.

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The purpose of this thesis is to develop new stability conditions for several linear dynamic systems, including linear parameter-varying (LPV), time-delay systems (LPVTD), slow LPV systems, and parameter-dependent linear time invariant (LTI) systems. These stability conditions are less conservative and/or computationally easier to apply than existing ones. This dissertation is composed of four parts. In the first part of this thesis, the complete stability domain for LTI parameter-dependent (LTIPD) systems is synthesized by extending existing results in the literature. This domain is calculated through a guardian map which involves the determinant of the Kronecker sum of a matrix with itself. The stability domain is synthesized for both single- and multi-parameter dependent LTI systems. The single-parameter case is easily computable, whereas the multi-parameter case is more involved. The determinant of the bialternate sum of a matrix with itself is also exploited to reduce the computational complexity. In the second part of the thesis, a class of parameter-dependent Lyapunov functions is proposed, which can be used to assess the stability properties of single-parameter LTIPD systems in a non-conservative manner. It is shown that stability of LTIPD systems is equivalent to the existence of a Lyapunov function of a polynomial type (in terms of the parameter) of known, bounded degree satisfying two matrix inequalities. The bound of polynomial degree of the Lyapunov functions is then reduced by taking advantage of the fact that the Lyapunov matrices are symmetric. If the matrix multiplying the parameter is not full rank, the polynomial order can be reduced even further. It is also shown that checking the feasibility of these matrix inequalities over a compact set can be cast as a convex optimization problem. Such Lyapunov functions and stability conditions for affine single-parameter LTIPD systems are then generalized to single-parameter polynomially-dependent LTIPD systems and affine multi-parameter LTIPD systems. The third part of the thesis provides one of the first attempts to derive computationally tractable criteria for analyzing the stability of LPV time-delayed systems. It presents both delay-independent and delay-dependent stability conditions, which are derived using appropriately selected Lyapunov-Krasovskii functionals. According to the system parameter dependence, these functionals can be selected to obtain increasingly non-conservative results. Gridding techniques may be used to cast these tests as Linear Matrix Inequalities (LMI's). In cases when the system matrices depend affinely or quadratically on the parameter, gridding may be avoided. These LMI's can be solved efficiently using available software. A numerical example of a time-delayed system motivated by a metal removal process is used to demonstrate the theoretical results. In the last part of the thesis, topics for future investigation are proposed. Among the most interesting avenues for research in this context, it is proposed to extend the existing stability analysis results to controller synthesis, which will be based on the same Lyapunov functions used to derive the nonconservative stability conditions. While designing the dynamic ontroller for linear and parameter-dependent systems, it is desired to take the advantage of the rank deficiency of the system matrix multiplying the parameter such that the controller is of lower dimension, or rank deficient without sacrificing the performance of closed-loop systems.
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4

Wang, Feng. "Robust control of quasi-linear parameter-varying L2 point formation flying with uncertain parameters." Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/6933.

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Robust high precision control of spacecraft formation flying is one of the most important techniques required for high-resolution interferometry missions in the complex deep-space environment. The thesis is focussed on the design of an invariant stringent performance controller for the Sun-Earth L2 point formation flying system over a wide range of conditions while maintaining system robust stability in the presence of parametric uncertainties. A Quasi-Linear Parameter-Varying (QLPV) model, generated without approximation from the exact nonlinear model, is developed in this study. With this QLPV form, the model preserves the transparency of linear controller design while reflecting the nonlinearity of the system dynamics. The Polynomial Eigenstructure Assignment (PEA) approach used for Linear Time-Invariant (LTI) and Linear Parameter-Varying (LPV ) models is extended to use the QLPV model to perform a form of dynamic inversion for a broader class of nonlinear systems which guarantees specific system performance. The resulting approach is applied to the formation flying QLPV model to design a PEA controller which ensures that the closed-loop performance is independent of the operating point. Due to variation in system parameters, the performance of most closed-loop systems are subject to model uncertainties. This leads naturally to the need to assess the robust stability of nonlinear and uncertain systems. This thesis presents two approaches to this problem, in the first approach, a polynomial matrix method to analyse the robustness of Multiple-Input and Multiple-Output (MIMO) systems for an intersectingD-region,which can copewith time-invariant uncertain systems is developed. In the second approach, an affine parameterdependent Lyapunov function based Linear Matrix Inequality (LMI) condition is developed to check the robust D-stability of QLPV uncertain systems.
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5

Macatula, Romcholo Yulo. "Linear Parameter Uncertainty Quantification using Surrogate Gaussian Processes." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99411.

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Анотація:
We consider uncertainty quantification using surrogate Gaussian processes. We take a previous sampling algorithm and provide a closed form expression of the resulting posterior distribution. We extend the method to weighted least squares and a Bayesian approach both with closed form expressions of the resulting posterior distributions. We test methods on 1D deconvolution and 2D tomography. Our new methods improve on the previous algorithm, however fall short in some aspects to a typical Bayesian inference method.
Master of Science
Parameter uncertainty quantification seeks to determine both estimates and uncertainty regarding estimates of model parameters. Example of model parameters can include physical properties such as density, growth rates, or even deblurred images. Previous work has shown that replacing data with a surrogate model can provide promising estimates with low uncertainty. We extend the previous methods in the specific field of linear models. Theoretical results are tested on simulated computed tomography problems.
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6

Carter, Lance Huntington. "Linear parameter varying representations for nonlinear control design /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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7

Vazirinejad, Shamsedin. "Model identification and parameter estimation of stochastic linear models." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185037.

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Анотація:
It is well known that when the input variables of the linear regression model are subject to noise contamination, the model parameters can not be estimated uniquely. This, in the statistical literature, is referred to as the identifiability problem of the errors-in-variables models. Further, in linear regression there is an explicit assumption of the existence of a single linear relationship. The statistical properties of the errors-in-variables models under the assumption that the noise variances are either known or that they can be estimated are well documented. In many situations, however, such information is neither available nor obtainable. Although under such circumstances one can not obtain a unique vector of parameters, the space, Ω, of the feasible solutions can be computed. Additionally, assumption of existence of a single linear relationship may be presumptuous as well. A multi-equation model similar to the simultaneous-equations models of econometrics may be more appropriate. The goals of this dissertation are the following: (1) To present analytical techniques or algorithms to reduce the solution space, Ω, when any type of prior information, exact or relative, is available; (2) The data covariance matrix, Σ, can be examined to determine whether or not Ω is bounded. If Ω is not bounded a multi-equation model is more appropriate. The methodology for identifying the subsets of variables within which linear relations can feasibly exist is presented; (3) Ridge regression technique is commonly employed in order to reduce the ills caused by collinearity. This is achieved by perturbing the diagonal elements of Σ. In certain situations, applying ridge regression causes some of the coefficients to change signs. An analytical technique is presented to measure the amount of perturbation required to render such variables ineffective. This information can assist the analyst in variable selection as well as deciding on the appropriate model; (4) For the situations when Ω is bounded, a new weighted regression technique based on the computed upper bounds on the noise variances is presented. This technique will result in identification of a unique estimate of the model parameters.
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8

Hopkins, Mark A. "Pseudo-linear identification: optimal joint parameter and state estimation of linear stochastic MIMO systems." Diss., Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/53941.

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This dissertation presents a new method of simultaneous parameter and state estimation for linear, stochastic, discrete—time, multiple-input, multiple-output (MIMO) (B systems. This new method is called pseudo·Iinear identification (PLID), and extends an earlier method to the more general case where system input and output measurements are corrupted by noise. PLID can be applied to completely observable, completely controllable systems with known structure (i.e., known observability indexes) and unknown parameters. No assumptions on pole and zero locations are required; and no assumptions on relative degree are required, except that the system transfer functions must be strictly proper. Under standard gaussian assumptions on the various noises, for time-invariant systems in the class described above, it is proved that PLID is the optimal estimator (in the mean-square·error sense) of the states and the parameters, conditioned on the output measurements. It is also proved, under a reasonable assumption of persistent excitation, that the PLID parameter estimates converge a.e. to the true parameter values of the unknown system. For deterministic systems, it is proved that PLID exactly identifies the states and parameters in the minimum possible time, so—called deadbeat identification. The proof brings out an interesting relation between the estimate error propagation and the observability matrix of the time-varying extended system (the extended system incorporates the unknown parameters into the state vector). This relation gives rise to an intuitively appealing notion of persistent excitation. Some results of system identification simulations are presented. Several different cases are simulated, including a two-input, two-output system with non-minimum-phase zeros, and an unstable system. A comparison of PLID with the widely used extended Kalman filter is presented for a single-input, single·output system with near cancellation of a pole-zero pair. Results are also presented from simulations of the adaptive control of an unstable. two-input, two-output system In these simulations, PLID is used in a se1f—tuning regulator to identify the parameters needed to compute the feedback gain matrix, and (simultaneously) to estimate the system states, for the state feedback
Ph. D.
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9

Kayhan, Belgin. "Parameter Estimation In Generalized Partial Linear Modelswith Tikhanov Regularization." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612530/index.pdf.

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Анотація:
Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the entire GPLM. Such a particular representation of submodels provides both a better accuracy and a better stability (regularity) under noise in the data. We aim to smooth the nonparametric part of GPLM by using a modified form of Multiple Adaptive Regression Spline (MARS) which is very useful for high-dimensional problems and does not impose any specific relationship between the predictor and dependent variables. Instead, it can estimate the contribution of the basis functions so that both the additive and interaction effects of the predictors are allowed to determine the dependent variable. The MARS algorithm has two steps: the forward and backward stepwise algorithms. In the rst one, the model is built by adding basis functions until a maximum level of complexity is reached. On the other hand, the backward stepwise algorithm starts with removing the least significant basis functions from the model. In this study, we propose to use a penalized residual sum of squares (PRSS) instead of the backward stepwise algorithm and construct PRSS for MARS as a Tikhonov regularization problem. Besides, we provide numeric example with two data sets
one has interaction and the other one does not have. As well as studying the regularization of the nonparametric part, we also mention theoretically the regularization of the parametric part. Furthermore, we make a comparison between Infinite Kernel Learning (IKL) and Tikhonov regularization by using two data sets, with the difference consisting in the (non-)homogeneity of the data set. The thesis concludes with an outlook on future research.
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10

Bruyère, Lilian Henri André. "Robust parametric autopilot for quasi-linear parameter-varying missile." Thesis, Cranfield University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413400.

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Книги з теми "Linear parameter"

1

Weiss, Rüdiger. Parameter-free iterative linear solvers. Berlin: Akademie Verlag, 1996.

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2

Shin, Jong-Yeob. Linear parameter varying control for actuator failure. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2002.

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3

Briat, Corentin. Linear Parameter-Varying and Time-Delay Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44050-6.

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4

Sename, Olivier, Peter Gaspar, and József Bokor, eds. Robust Control and Linear Parameter Varying Approaches. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36110-4.

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5

White, Andrew P., Guoming Zhu, and Jongeun Choi. Linear Parameter-Varying Control for Engineering Applications. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5040-4.

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6

Koch, Karl-Rudolf. Parameter estimation and hypothesis testing in linear models. Berlin: Springer-Verlag, 1988.

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7

Koch, Karl-Rudolf. Parameter Estimation and Hypothesis Testing in Linear Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-02544-4.

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8

Tóth, Roland. Modeling and Identification of Linear Parameter-Varying Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13812-6.

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9

Koch, Karl-Rudolf. Parameter Estimation and Hypothesis Testing in Linear Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03976-2.

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10

Mohammadpour, Javad, and Carsten W. Scherer, eds. Control of Linear Parameter Varying Systems with Applications. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1833-7.

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Частини книг з теми "Linear parameter"

1

Ortner, Norbert, and Peter Wagner. "Parameter Integration." In Fundamental Solutions of Linear Partial Differential Operators, 181–250. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20140-5_3.

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2

Colosi, Tiberiu, Mihail-Ioan Abrudean, Mihaela-Ligia Unguresan, and Vlad Muresan. "Time-Varying Linear Processes." In Numerical Simulation of Distributed Parameter Processes, 11–15. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00014-5_2.

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3

Fischer, Bernd. "Parameter Free Methods." In Polynomial Based Iteration Methods for Symmetric Linear Systems, 155–211. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-663-11108-5_6.

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4

Fischer, Bernd. "Parameter Dependent Methods." In Polynomial Based Iteration Methods for Symmetric Linear Systems, 212–23. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-663-11108-5_7.

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5

Colosi, Tiberiu, Mihail-Ioan Abrudean, Mihaela-Ligia Unguresan, and Vlad Muresan. "Linear Processes Invariant in Time." In Numerical Simulation of Distributed Parameter Processes, 3–10. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00014-5_1.

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6

Colosi, Tiberiu, Mihail-Ioan Abrudean, Mihaela-Ligia Unguresan, and Vlad Muresan. "Linear Processes with Distributed Parameters." In Numerical Simulation of Distributed Parameter Processes, 25–43. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00014-5_4.

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7

Morris, Kirsten A. "Optimal Linear-Quadratic Controller Design." In Controller Design for Distributed Parameter Systems, 103–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34949-3_4.

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8

Koch, Karl-Rudolf. "Parameter Estimation in Linear Models." In Parameter Estimation and Hypothesis Testing in Linear Models, 175–296. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-02544-4_4.

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9

Koch, Karl-Rudolf. "Parameter Estimation in Linear Models." In Parameter Estimation and Hypothesis Testing in Linear Models, 149–269. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03976-2_4.

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10

Wohlfarth, Ch. "Solubility parameter of polyethylene, linear." In Polymer Solutions, 1571. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-02890-8_922.

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Тези доповідей конференцій з теми "Linear parameter"

1

Carlson, Fredrik Bagge, Anders Robertsson, and Rolf Johansson. "Linear parameter-varying spectral decomposition." In 2017 American Control Conference (ACC). IEEE, 2017. http://dx.doi.org/10.23919/acc.2017.7962945.

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2

Hanema, Jurre, Roland Toth, and Mircea Lazar. "Stabilizing non-linear MPC using linear parameter-varying representations." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8264185.

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3

Schouten, Sil, Daming Lou, and Siep Weiland. "Model reduction for linear parameter-varying systems through parameter projection." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029318.

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4

Butcher, Mark, and Alireza Karimi. "Linear Parameter Varying Iterative Learning Control." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400885.

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5

Narimani, A., and M. F. Golnaraghi. "Parameter Optimizing for Piecewise Linear Isolator." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-61312.

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Анотація:
In this paper we have studied the effect of end stops in an isolator. While subjected to excessive base excitation the stops prevent the system from excessive relative displacement particularly around the resonance frequency. Although stoppers prevent the undesired motion they increase the transmitted force that is undesirable in suspension systems. This system is modeled as a piecewise linear system where the nonlinearity cannot be considered small. Therefore we have adopted an averaging method which leads to analytical frequency response of the piecewise linear system at resonance. Using this analytical method we are able to obtain the range of the parameters, which minimize the relative displacement of the system. Further more using the RMS optimization methods the transmitted force in the system is optimized.
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6

Shen, Ying, and Hui Zhang. "Parameter identifiability of quantized linear systems." In 2012 10th World Congress on Intelligent Control and Automation (WCICA 2012). IEEE, 2012. http://dx.doi.org/10.1109/wcica.2012.6358412.

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7

M. Stovas, A. "Parameter Estimation for Linear Velocity Functions." In 68th EAGE Conference and Exhibition incorporating SPE EUROPEC 2006. European Association of Geoscientists & Engineers, 2006. http://dx.doi.org/10.3997/2214-4609.201402415.

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8

Doweck, Yaron, Alon Amar, and Israel Cohen. "Parameter estimation of harmonic linear chirps." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362629.

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9

Shen, Liran, Qingbo Yin, Tian'en Shen, and Lili Guo. "Parameter estimation for linear FM signal." In 2012 5th International Congress on Image and Signal Processing (CISP). IEEE, 2012. http://dx.doi.org/10.1109/cisp.2012.6470027.

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10

Ploskas, Nikolaos. "Parameter Tuning of Linear Programming Solvers." In 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM). IEEE, 2022. http://dx.doi.org/10.1109/seeda-cecnsm57760.2022.9933002.

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Звіти організацій з теми "Linear parameter"

1

Birx, D. L., G. J. Caporaso, and L. L. Reginato. Linear induction accelerator parameter options. Office of Scientific and Technical Information (OSTI), April 1986. http://dx.doi.org/10.2172/5331064.

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2

Blackwell, J. A Maximum Likelihood Parameter Estimation Program for General Non-Linear Systems. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada192703.

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3

Berg, J. S. Parameter choices for a muon recirculating linear accelerator from 5 to 63 GeV. Office of Scientific and Technical Information (OSTI), June 2014. http://dx.doi.org/10.2172/1149437.

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4

Tunc Aldemir, Don W. Miller, Brian k. Hajek, and Peng Wang. Development of a Probabilistic Technique for On-line Parameter and State Estimation in Non-linear Dynamic Systems. Office of Scientific and Technical Information (OSTI), April 2002. http://dx.doi.org/10.2172/793324.

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5

Hauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, January 2023. http://dx.doi.org/10.26509/frbc-wp-202305.

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In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference using our model is straightforward because, in treating the parameters rather than the variables nonparametrically, the model remains conditionally linear in the mean. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflationary measures vary nonlinearly with movements in uncertainty.
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6

Parzen, George. The Linear Parameters and the Decoupling Matrix for Linearly Coupled Motion in 6 Dimensional Phase Space. Office of Scientific and Technical Information (OSTI), March 1995. http://dx.doi.org/10.2172/1119386.

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7

Lohse, T., and P. Emma. Linear fitting of BPM orbits and lattice parameters. Office of Scientific and Technical Information (OSTI), February 1989. http://dx.doi.org/10.2172/6356544.

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8

Kundu, Debasis, and Amit Mitra. Estimating the Parameters of the Linear Compartment Model. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada358190.

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9

Ghosh, Subir. Influential Nonegligible Parameters under the Search Linear Model. Fort Belvoir, VA: Defense Technical Information Center, April 1986. http://dx.doi.org/10.21236/ada170079.

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10

Ghosh, Subir. Influential Nonnegligible Parameters Under the Search Linear Model. Fort Belvoir, VA: Defense Technical Information Center, April 1986. http://dx.doi.org/10.21236/ada172007.

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