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Journal articles on the topic 'Linear parameter'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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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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11

Lomovtsev, F. E. "Parameter differentiation of linear operators with a parameter-dependent domain." Doklady Mathematics 86, no. 1 (July 2012): 571–73. http://dx.doi.org/10.1134/s1064562412040436.

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12

Zobitz, J. M., T. Quaife, and N. K. Nichols. "Efficient hyper-parameter determination for regularised linear BRDF parameter retrieval." International Journal of Remote Sensing 41, no. 4 (September 26, 2019): 1437–57. http://dx.doi.org/10.1080/01431161.2019.1667552.

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13

Han, Seungyong, and Sangmoon Lee. "Sampled‐parameter dependent stabilization for linear parameter varying systems with asynchronous parameter sampling." International Journal of Robust and Nonlinear Control 31, no. 8 (February 24, 2021): 3279–309. http://dx.doi.org/10.1002/rnc.5454.

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14

Pourboghrat, Farzad, and Dong Hak Chyung. "Parameter identification of linear delay systems." International Journal of Control 49, no. 2 (February 1989): 595–627. http://dx.doi.org/10.1080/00207178908559656.

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15

Geromel, J. C. "Optimal linear filtering under parameter uncertainty." IEEE Transactions on Signal Processing 47, no. 1 (1999): 168–75. http://dx.doi.org/10.1109/78.738249.

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16

Ragot, Jose, Didier Maquin, and Olivier Adrot. "PARAMETER UNCERTAINTIES CHARACTERISATION FOR LINEAR MODELS." IFAC Proceedings Volumes 39, no. 13 (2006): 581–86. http://dx.doi.org/10.3182/20060829-4-cn-2909.00096.

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17

Liu, Lei, and Guanzhong Hu. "A Parameter-Free Linear Sampling Method." IEEE Access 7 (2019): 17935–40. http://dx.doi.org/10.1109/access.2019.2896309.

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18

Shimura, Takaki. "Ultrasonic non‐linear parameter measuring system." Journal of the Acoustical Society of America 86, no. 2 (August 1989): 863. http://dx.doi.org/10.1121/1.398121.

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19

Prato, Domingo, and Constantino Tsallis. "Functions of linear operators: Parameter differentiation." Journal of Mathematical Physics 41, no. 5 (May 2000): 3278–82. http://dx.doi.org/10.1063/1.533305.

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20

GENTILE, C., and M. PRIMO. "Parameter dependent quasi-linear parabolic equations." Nonlinear Analysis 59, no. 5 (November 2004): 801–12. http://dx.doi.org/10.1016/s0362-546x(04)00292-5.

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21

Gentile, Cláudia Buttarello, and Marcos Roberto Teixeira Primo. "Parameter dependent quasi-linear parabolic equations." Nonlinear Analysis: Theory, Methods & Applications 59, no. 5 (November 2004): 801–12. http://dx.doi.org/10.1016/j.na.2004.07.040.

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22

Paraev, Yu I., and S. A. Tsvetnitskaya. "Linear System Parameter and State Estimation." IFAC Proceedings Volumes 19, no. 5 (May 1986): 173–76. http://dx.doi.org/10.1016/s1474-6670(17)59788-8.

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23

Bonett, Douglas G., and J. Arthur Woodward. "Parameter estimation in linear multinomial models." Computational Statistics & Data Analysis 7, no. 2 (December 1988): 179–87. http://dx.doi.org/10.1016/0167-9473(88)90091-6.

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24

Molchanyuk, I. V., and A. V. Plotnikov. "Linear control systems with fuzzy parameter." Nonlinear Oscillations 9, no. 1 (January 2006): 59–64. http://dx.doi.org/10.1007/s11072-006-0025-2.

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25

Shetty, Sandeep, Akhter Husain, Parag Majithia, and Suhail Uddin. "YEN-Linear: A sagittal cephalometric parameter." Journal of the World Federation of Orthodontists 2, no. 2 (June 2013): e57-e60. http://dx.doi.org/10.1016/j.ejwf.2013.03.004.

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26

Karadzhov, Yuri. "Matrix superpotential linear in variable parameter." Communications in Nonlinear Science and Numerical Simulation 17, no. 4 (April 2012): 1522–28. http://dx.doi.org/10.1016/j.cnsns.2011.09.025.

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27

Rades, M. "Literature Review : Linear Model Parameter Estimation." Shock and Vibration Digest 25, no. 5 (May 1, 1993): 3–15. http://dx.doi.org/10.1177/058310249302500501.

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28

Bergemann, T. L., and D. B. Clarkson. "Linear Parameter Haplotype Models with Stratification." Human Heredity 59, no. 4 (2005): 201–9. http://dx.doi.org/10.1159/000086698.

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29

Bamieh, Bassam, and Laura Giarré. "Identification of linear parameter varying models." International Journal of Robust and Nonlinear Control 12, no. 9 (July 15, 2002): 841–53. http://dx.doi.org/10.1002/rnc.706.

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30

Szabó, Z., and J. Bokor. "Transformations for linear parameter varying systems." IFAC-PapersOnLine 51, no. 26 (2018): 87–93. http://dx.doi.org/10.1016/j.ifacol.2018.11.163.

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31

Jacquez, J. A., and T. Perry. "Parameter estimation: local identifiability of parameters." American Journal of Physiology-Endocrinology and Metabolism 258, no. 4 (April 1, 1990): E727—E736. http://dx.doi.org/10.1152/ajpendo.1990.258.4.e727.

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For biological systems one often cannot set up experiments to measure all of the state variables. If only a subset of the state variables can be measured, it is possible that some of the system parameters cannot influence the measured state variables or that they do so in combinations that do not define the parameters' effects separately. Such parameters are unidentifiable and are in theory unestimable. Given a model of the system, linear or nonlinear, and initial estimates of the values of all parameters, we exhibit a simple theory and describe a program for checking the local identifiability of the parameters at the initial estimates for given experiments on the model. The program, IDENT, is available from the authors.

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32

Efremov, A. "A Linear Approach for Parameters Estimation of Multivariable Models in a Parameter Matrix Form." Information Technologies and Control 11, no. 3 (September 1, 2014): 36–44. http://dx.doi.org/10.2478/itc-2013-0014.

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Abstract When a model output is a linear function of the model parameters, the estimation process is significantly simplified, since the optimal estimates can be determined without the usage of a numerical optimization method. Moreover, some types of nonlinear models w.r.t. their parameters can be interpreted as linear (obviously introducing a discrepancy). This is the main premise behind the linear approach for parameter estimation, where the Least Squares (LS) method is used for parameters estimation. As this assumption contradicts with the non-linear parameterized model structure, the estimation process becomes iterative. In spite of this, the linear approach is frequently preferable due to the reduced number of computations, compared with the non-linear approach, where the model is correctly considered as non-linear. Moreover, some issues with the starting point selection, stuck at a local minima, etc., natural for the non-linear approach, are avoided. In this paper estimators are presented, based on the linear approach, for both MIMO linear and non-linear parameterized models in a parameter matrix form. The representatives of the first group are LS and Weighted LS (WLS). For non-linear models, this approach is presented in terms of Extended LS (ELS). The topic regarding the efficient realizations of the estimators is also discussed

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33

Florens, Jean-Pierre, and Anna Simoni. "REGULARIZING PRIORS FOR LINEAR INVERSE PROBLEMS." Econometric Theory 32, no. 1 (November 6, 2014): 71–121. http://dx.doi.org/10.1017/s0266466614000796.

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This paper proposes a new Bayesian approach for estimating, nonparametrically, functional parameters in econometric models that are characterized as the solution of a linear inverse problem. By using a Gaussian process prior we propose the posterior mean as an estimator and prove frequentist consistency of the posterior distribution. The latter provides the frequentist validation of our Bayesian procedure. We show that the minimax rate of contraction of the posterior distribution can be obtained provided that either the regularity of the prior matches the regularity of the true parameter or the prior is scaled at an appropriate rate. The scaling parameter of the prior distribution plays the role of a regularization parameter. We propose a new data-driven method for optimally selecting in practice this regularization parameter. We also provide sufficient conditions such that the posterior mean, in a conjugate-Gaussian setting, is equal to a Tikhonov-type estimator in a frequentist setting. Under these conditions our data-driven method is valid for selecting the regularization parameter of the Tikhonov estimator as well. Finally, we apply our general methodology to two leading examples in econometrics: instrumental regression and functional regression estimation.

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34

Bouazizi, Mohamed Hechmi. "An observer-based H∞ linear parameter varying controller for time delayed linear parameter varying systems using dilated linear matrix inequalities and Wirtinger inequality." Transactions of the Institute of Measurement and Control 43, no. 9 (January 26, 2021): 1915–23. http://dx.doi.org/10.1177/0142331220983629.

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In this study, we give a method for the design of linear parameter varying (LPV) observers in order to perform an LPV time delayed state feedback control for LPV systems with time varying delay. We derive some tractable analysis and synthesis conditions expressed in terms of linear matrix inequalities (LMIs). We show how it is possible to reduce significantly the conservatism of the quadratic approach by using parameter dependent Lyapunov-Krasovskii functional and LMI dilation techniques jointed to the Wirtinger integral inequality. We also present a method that makes it possible to do without the separation principle when determining the observer and the state feedback parameters. The synthesis problem is formulated without this principle. A numerical example is provided to illustrate the effectiveness of our approach that leads to a better H∞ level compared with other results, from literature, for the same example.

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35

Malloy, David, and B. C. Chang. "Stabilizing Controller Design for Linear Parameter-Varying Systems Using Parameter Feedback." Journal of Guidance, Control, and Dynamics 21, no. 6 (November 1998): 891–98. http://dx.doi.org/10.2514/2.4322.

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36

Altun, Yusuf, and Kayhan Gulez. "Linear parameter varying feedforward control synthesis using parameter-dependent Lyapunov function." Nonlinear Dynamics 78, no. 4 (August 12, 2014): 2293–307. http://dx.doi.org/10.1007/s11071-014-1544-5.

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37

Stovba, Viktor. "Ellipsoid Method for Linear Regression Parameters Determination." Cybernetics and Computer Technologies, no. 3 (October 27, 2020): 14–24. http://dx.doi.org/10.34229/2707-451x.20.3.2.

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Introduction. Linear regression parameters determination can be formulated as a non-smooth function minimization problem, which is Lp-norm of residual of the linear equations system. To solve it non-smooth function minimization methods can be used, e.g. subgradient methods. The article [7] considers ellipsoid method application for finding Lp-solution of redefined linear equations system with 1≤p≤2. The purpose of the paper is to extend the algorithm based on the ellipsoid method for a linear regression parameters determination problem with an arbitrary value of parameter p≥2 so that under big values of p the solution of the problem equals minimax method solution, which corresponds to p=∞ case. To describe the formulation of observation approximation problem with quadratic function as linear regression parameters determination problem. To analyze algorithm work results for great number of observations and outliers. To compare the minimax method and the ellipsoid method algorithm work results for linear regression parameters determination problem with big values of parameter p. Results. The way of calculation of objective function and its subgradient values with large values of parameter p was developed and verified on example of observation approximation containing outliers with linear function. Algorithm based on ellipsoid method changes linear function parameters monotonically using parameter p adjusting, thereby permits to reject or consider these or those observations. It is shown in [3] that Least Absolute Deviations method (LAD) is advised to be used as far as it ignores outliers and reconstructs linear function accurately. Experiment results with big number of observations and outliers using p=1 confirmed that conclusion: LAD ignores outlier groups and approximates observations with linear function adequately. Least Square Method (LSM) deviates from optimal linear function if a group of outliers is present in particular area. In case of using big values of parameter p problem solution converges to minimax method solution. Conclusions. Algorithm based on ellipsoid method permits to determine linear regression parameters with arbitrary value of parameter p≥1. So, three known methods can be used – LAD, LSM and minimax method – as its special cases. Moreover, directing p to 1, intensity of outliers ignoring can be regulated, that gives a possibility to use external sources of information (expert opinions, measuring devices readings, statistical forecasts, etc.) for more correct and adequate approximation function reconstruction. Keywords: ellipsoid method, linear regression, outliers.

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38

Wirth, Fabian. "THE GELFAND FORMULA FOR LINEAR PARAMETER-VARYING AND LINEAR SWITCHING SYSTEMS." IFAC Proceedings Volumes 38, no. 1 (2005): 495–500. http://dx.doi.org/10.3182/20050703-6-cz-1902.00653.

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39

Li, Jr-Shin, and Ji Qi. "Ensemble Control of Time-Invariant Linear Systems with Linear Parameter Variation." IEEE Transactions on Automatic Control 61, no. 10 (October 2016): 2808–20. http://dx.doi.org/10.1109/tac.2015.2503698.

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40

SU, TE-JEN, I.-KONG FONG, TE-SON KUO, and YORK-YTH SUN. "Robust stability for linear time-delay systems with linear parameter perturbations." International Journal of Systems Science 19, no. 10 (January 1988): 2123–29. http://dx.doi.org/10.1080/00207728808964104.

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41

Kim, W.-J., B.-Y. Lee, and Y.-S. Park. "Non-linear joint parameter identification using the frequency response function of the linear substructure." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 218, no. 9 (September 1, 2004): 947–55. http://dx.doi.org/10.1243/0954406041991314.

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A method based on frequency domain approaches is presented for the non-linear parameter identification of a structure having non-linear joints. The frequency response function (FRF) of the linear substructure, which can be calculated from the finite element method or measured by an experimental method, is used to calculate its FRFs needed in the parameter identification process. This method is easily applicable to a complex real structure having non-linear joints since it uses the FRF of the substructure. Since this method is performed in the frequency domain, the number of equations can be easily increased to as many as required to identify unknown parameters, not only by just varying the excitation amplitude but also by selecting the excitation frequencies. The validity of this method was tested numerically and experimentally with a cantilever beam having a non-linear element. It was verified through examples that the proposed method is useful to identify the non-linear joint parameters of a structure having arbitrary boundaries.

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42

Schulze, Hans-Henning, and Dirk Vorberg. "Linear Phase Correction Models for Synchronization: Parameter Identification and Estimation of Parameters." Brain and Cognition 48, no. 1 (February 2002): 80–97. http://dx.doi.org/10.1006/brcg.2001.1305.

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43

Asem, Pouyan, Heloise Fuselier, and Joseph F. Labuz. "On a Four-Parameter Linear Failure Criterion." Rock Mechanics and Rock Engineering 54, no. 6 (March 30, 2021): 3369–76. http://dx.doi.org/10.1007/s00603-021-02451-w.

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44

Russell, David. "Structural parameter optimization of linear elastic systems." Communications on Pure & Applied Analysis 10, no. 5 (2011): 1517–36. http://dx.doi.org/10.3934/cpaa.2011.10.1517.

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45

Rachmawati, Baiq Devi. "Estimasi Parameter Regresi Linear Menggunakan Regresi Kuantil." EIGEN MATHEMATICS JOURNAL 2, no. 2 (December 29, 2018): 37. http://dx.doi.org/10.29303/emj.v2i2.15.

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46

Li, L. "Model Reduction for Linear Parameter-Dependent Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 4048–53. http://dx.doi.org/10.3182/20080706-5-kr-1001.00681.

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47

Balenzuela, Mark P., Adrian G. Wills, Christopher Renton, and Brett Ninness. "Parameter estimation for Jump Markov Linear Systems." Automatica 135 (January 2022): 109949. http://dx.doi.org/10.1016/j.automatica.2021.109949.

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48

Emara-Shabaik, Hosam E. "Noise-Robust Parameter Estimation of Linear Systems." Journal of Vibration and Control 6, no. 5 (July 2000): 727–40. http://dx.doi.org/10.1177/107754630000600505.

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49

Grenaille, Sylvain, David Henry, and All Zolghadri. "FAULT DIAGNOSIS IN LINEAR PARAMETER VARYING SYSTEMS." IFAC Proceedings Volumes 39, no. 9 (2006): 107–12. http://dx.doi.org/10.3182/20060705-3-fr-2907.00020.

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50

Gutierrez, M., R. C. Degeneff, P. J. McKenny, and J. M. Schneider. "Linear, lumped parameter transformer model reduction technique." IEEE Transactions on Power Delivery 10, no. 2 (April 1995): 853–61. http://dx.doi.org/10.1109/61.400845.

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