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Journal articles on the topic 'STOCHASTIC SENSITIVITY'

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Published: 4 June 2021
Last updated: 11 February 2022

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1

Eschenbach, Ted G., and Robert J. Gimpel. "Stochastic Sensitivity Analysis." Engineering Economist 35, no. 4 (January 1990): 305–21. http://dx.doi.org/10.1080/00137919008903024.

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2

Irving, A. D. "Stochastic sensitivity analysis." Applied Mathematical Modelling 16, no. 1 (January 1992): 3–15. http://dx.doi.org/10.1016/0307-904x(92)90110-o.

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3

Zhou, Peiyuan, and Jinling Wang. "Stochastic Ionosphere Models for Precise GNSS Positioning: Sensitivity Analysis." Journal of Global Positioning Systems 12, no. 1 (June 30, 2013): 53–60. http://dx.doi.org/10.5081/jgps.12.1.53.

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4

Bashkirtseva, I. A., and L. B. Ryashko. "Stochastic sensitivity of 3D-cycles." Mathematics and Computers in Simulation 66, no. 1 (June 2004): 55–67. http://dx.doi.org/10.1016/j.matcom.2004.02.021.

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5

Ryashko, L. B., and I. A. Bashkirtseva. "On control of stochastic sensitivity." Automation and Remote Control 69, no. 7 (July 2008): 1171–80. http://dx.doi.org/10.1134/s0005117908070084.

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6

McClendon, Marvin, and Herschel Rabitz. "Sensitivity analysis in stochastic mechanics." Physical Review A 37, no. 9 (May 1, 1988): 3493–98. http://dx.doi.org/10.1103/physreva.37.3493.

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7

Kundu, Ajanta, and Sandip Sarkar. "Stochastic resonance in visual sensitivity." Biological Cybernetics 109, no. 2 (November 15, 2014): 241–54. http://dx.doi.org/10.1007/s00422-014-0638-y.

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8

Römisch, Werner, and Rüdiger Schultz. "Distribution sensitivity in stochastic programming." Mathematical Programming 50, no. 1-3 (March 1991): 197–226. http://dx.doi.org/10.1007/bf01594935.

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9

Luo, Mei-Ju, and Yuan Lu. "Properties of Expected Residual Minimization Model for a Class of Stochastic Complementarity Problems." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/497586.

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Expected residual minimization (ERM) model which minimizes an expected residual function defined by an NCP function has been studied in the literature for solving stochastic complementarity problems. In this paper, we first give the definitions of stochasticP-function, stochasticP0-function, and stochastic uniformlyP-function. Furthermore, the conditions such that the function is a stochasticPP0-function are considered. We then study the boundedness of solution set and global error bounds of the expected residual functions defined by the “Fischer-Burmeister” (FB) function and “min” function. The conclusion indicates that solutions of the ERM model are robust in the sense that they may have a minimum sensitivity with respect to random parameter variations in stochastic complementarity problems. On the other hand, we employ quasi-Monte Carlo methods and derivative-free methods to solve ERM model.
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10

Grzywiński, Maksym. "Stochastic Sensitivity Analysis of Cylindrical Shell." Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series. 16, no. 2 (December 1, 2016): 35–42. http://dx.doi.org/10.1515/tvsb-2016-0012.

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Abstract The paper deals with some chosen aspects of stochastic sensitivity structural analysis and its application in the engineering practice. The main aim of the study is to provide the generalized stochastic perturbation technique based on classical Taylor expansion with a single random variable. The study is illustrated by numerical results concerning an industrial thin shell structure modeled as a 3-D structure.
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11

Gunawan, Rudiyanto, Yang Cao, Linda Petzold, and Francis J. Doyle. "Sensitivity Analysis of Discrete Stochastic Systems." Biophysical Journal 88, no. 4 (April 2005): 2530–40. http://dx.doi.org/10.1529/biophysj.104.053405.

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12

Lowen, Philip D. "Parameter sensitivity in stochastic optimal control∗." Stochastics 22, no. 1 (September 1987): 1–40. http://dx.doi.org/10.1080/17442508708833465.

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13

Lam, Henry. "Robust Sensitivity Analysis for Stochastic Systems." Mathematics of Operations Research 41, no. 4 (November 2016): 1248–75. http://dx.doi.org/10.1287/moor.2015.0776.

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14

Chowdhury, R., and S. Adhikari. "Stochastic sensitivity analysis using preconditioning approach." Engineering Computations 27, no. 7 (October 12, 2010): 841–62. http://dx.doi.org/10.1108/02644401011073683.

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15

Hien, T. D., and M. Kleiber. "Stochastic design sensitivity in structural dynamics." International Journal for Numerical Methods in Engineering 32, no. 6 (October 25, 1991): 1247–65. http://dx.doi.org/10.1002/nme.1620320606.

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16

Glazebrook, K. D. "Sensitivity Analysis for Stochastic Scheduling Problems." Mathematics of Operations Research 12, no. 2 (May 1987): 205–23. http://dx.doi.org/10.1287/moor.12.2.205.

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17

KODA, MASATO. "Sensitivity analysis of stochastic dynamical systems." International Journal of Systems Science 23, no. 12 (December 1992): 2187–95. http://dx.doi.org/10.1080/00207729208949448.

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18

SOCHA, LESLAW. "Decentralized sensitivity of stochastic composite systems." International Journal of Systems Science 24, no. 1 (January 1993): 13–31. http://dx.doi.org/10.1080/00207729308949469.

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19

Bod’a, Martin. "Stochastic sensitivity analysis of concentration measures." Central European Journal of Operations Research 25, no. 2 (January 10, 2017): 441–71. http://dx.doi.org/10.1007/s10100-016-0465-4.

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20

Lachout, Petr. "Stochastic optimisation: sensitivity and Delta Theorem." PAMM 5, no. 1 (December 2005): 725–26. http://dx.doi.org/10.1002/pamm.200510337.

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21

Akpan, U. O., and M. R. Kujath. "Sensitivity of a Mobile Manipulator Response to System Parameters." Journal of Vibration and Acoustics 120, no. 1 (January 1, 1998): 156–63. http://dx.doi.org/10.1115/1.2893799.

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The reported study investigates the stochastic dynamics of a mobile manipulator. The manipulator is mounted atop a vehicle supported on multiple wheels. Stochastic excitations and deterministic excitation of the manipulator are induced by the acceleration of the vehicle on a traction surface. The stochastic excitations are related by time delays due to the longitudinal alignment of the vehicle wheels. Expressions for the deterministic and covariance matrices of the manipulator stochastic motions are developed. Sensitivity of the system responses to the surface roughness, the dynamics and the kinematics parameters of the manipulator is investigated. Suggestions for mobile manipulator design to minimize the influence of the stochastic base excitations are presented.
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22

Wang, Guihua, Liang Wang, Yujing Han, Shuo Zhou, and Xiyun Guan. "Nanopore Stochastic Detection: Diversity, Sensitivity, and Beyond." Accounts of Chemical Research 46, no. 12 (April 24, 2013): 2867–77. http://dx.doi.org/10.1021/ar400031x.

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23

Bashkirtseva, I. A., and L. B. Ryashko. "On stochastic sensitivity control in discrete systems." Automation and Remote Control 71, no. 9 (September 2010): 1833–48. http://dx.doi.org/10.1134/s0005117910090079.

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24

Bashkirtseva, I. A., D. R. Nurmukhametova, and L. B. Ryashko. "On controlling stochastic sensitivity of oscillatory systems." Automation and Remote Control 74, no. 6 (June 2013): 932–43. http://dx.doi.org/10.1134/s0005117913060040.

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25

Seki, K., V. Balakrishnan, and G. Nicolis. "Sensitivity to initial conditions in stochastic systems." Physical Review E 47, no. 1 (January 1, 1993): 155–63. http://dx.doi.org/10.1103/physreve.47.155.

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26

Flynn, Thomas. "Forward sensitivity analysis for contracting stochastic systems." Advances in Applied Probability 50, no. 01 (March 2018): 102–30. http://dx.doi.org/10.1017/apr.2018.6.

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Abstract In this paper we investigate gradient estimation for a class of contracting stochastic systems on a continuous state space. We find conditions on the one-step transitions, namely differentiability and contraction in a Wasserstein distance, that guarantee differentiability of stationary costs. Then we show how to estimate the derivatives, deriving an estimator that can be seen as a generalization of the forward sensitivity analysis method used in deterministic systems. We apply the results to examples, including a neural network model.
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27

Bashkirtseva, Irina. "Attainability analysis in the stochastic sensitivity control." International Journal of Control 88, no. 2 (September 2, 2014): 276–84. http://dx.doi.org/10.1080/00207179.2014.949870.

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28

Ariaratnam, S. T., and Wei-Chau Xie. "Sensitivity of pitchfork bifurcation to stochastic perturbation." Dynamics and Stability of Systems 7, no. 3 (January 1992): 139–50. http://dx.doi.org/10.1080/02681119208806132.

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29

Bashkirtseva, I. "Stochastic sensitivity analysis: theory and numerical algorithms." IOP Conference Series: Materials Science and Engineering 192 (April 2017): 012024. http://dx.doi.org/10.1088/1757-899x/192/1/012024.

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30

Fleming, Wendell H., Hidehiro Kaise, and Shuenn-Jyi Sheu. "Max-Plus Stochastic Control and Risk-Sensitivity." Applied Mathematics and Optimization 62, no. 1 (February 6, 2010): 81–144. http://dx.doi.org/10.1007/s00245-010-9097-6.

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31

Hien, T. D., and M. Kleiber. "Stochastic structural design sensitivity of static response." Computers & Structures 38, no. 5-6 (January 1991): 659–67. http://dx.doi.org/10.1016/0045-7949(91)90017-g.

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32

Song, Datong, Suhuan Chen, and Zhipin Qiu. "Stochastic sensitivity analysis of eigenvalues and eigenvectors." Computers & Structures 54, no. 5 (March 1995): 891–96. http://dx.doi.org/10.1016/0045-7949(94)00386-h.

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33

Kreimer, Joseph. "Generalized sensitivity analysis of ergodic stochastic systems." Mathematics and Computers in Simulation 31, no. 1-2 (February 1989): 123–36. http://dx.doi.org/10.1016/0378-4754(89)90057-8.

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34

Whittle, P. "Risk-sensitivity, large deviations and stochastic control." European Journal of Operational Research 73, no. 2 (March 1994): 295–303. http://dx.doi.org/10.1016/0377-2217(94)90266-6.

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35

Dupačová, Jitka. "On statistical sensitivity analysis in stochastic programming." Annals of Operations Research 30, no. 1 (December 1991): 199–214. http://dx.doi.org/10.1007/bf02204817.

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36

Dupačová, Jitka. "Stability and sensitivity-analysis for stochastic programming." Annals of Operations Research 27, no. 1 (December 1990): 115–42. http://dx.doi.org/10.1007/bf02055193.

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37

Sluzalec, Andrzej. "Stochastic shape sensitivity in powder metallurgy processing." Applied Mathematical Modelling 36, no. 8 (August 2012): 3743–52. http://dx.doi.org/10.1016/j.apm.2011.11.015.

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38

Sakouvogui, Kekoura, Saleem Shaik, Curt Doetkott, and Rhonda Magel. "Sensitivity analysis of stochastic frontier analysis models." Monte Carlo Methods and Applications 27, no. 1 (February 2, 2021): 71–90. http://dx.doi.org/10.1515/mcma-2021-2083.

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Abstract The efficiency measures of the Stochastic Frontier Analysis (SFA) models are dependent on distributional assumptions of the one-sided error or inefficiency term. Given the intent of earlier researchers in the evaluation of a single inefficiency distribution using Monte Carlo (MC) simulation, much attention has not been paid to the comparative analysis of SFA models. Our paper aims to evaluate the effects of the assumption of the inefficiency distribution and thus compares different SFA model assumptions by conducting a MC simulation. In this paper, we derive the population statistical parameters of truncated normal, half-normal, and exponential inefficiency distributions of SFA models with the objective of having comparable sample mean and sample standard deviation during MC simulation. Thus, MC simulation is conducted to evaluate the statistical properties and robustness of the inefficiency distributions of SFA models and across three different misspecification scenarios, sample sizes, production functions, and input distributions. MC simulation results show that the misspecified truncated normal SFA model provides the smallest mean absolute deviation and mean square error when the true data generating process is a half-normal inefficiency distribution.
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39

Weirs, V. Gregory, Laura P. Swiler, William J. Rider, Brian M. Adams, James R. Kamm, and Michael S. Eldred. "Sensitivity analysis using stochastic expansion methods to calculate variance-based sensitivity indices." Procedia - Social and Behavioral Sciences 2, no. 6 (2010): 7765–67. http://dx.doi.org/10.1016/j.sbspro.2010.05.218.

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40

YU. RYAGIN, MIKHAIL, and LEV B. RYASHKO. "THE ANALYSIS OF THE STOCHASTICALLY FORCED PERIODIC ATTRACTORS FOR CHUA'S CIRCUIT." International Journal of Bifurcation and Chaos 14, no. 11 (November 2004): 3981–87. http://dx.doi.org/10.1142/s0218127404011600.

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This report shows the results of sensitivity analysis for Chua's circuit periodic attractors under small disturbances. Sensitivity analysis is based on the quasipotential method. Quasipotential's first approximation in the neighborhood of the limit cycle is defined by the matrix of orbital quadratic form, named stochastic sensitivity function (SSF). SSF is defined for the points of the nonperturbed limit cycle and can be computed using the numerical algorithm. Stochastic sensitivity of the limit cycles for the Chua's circuit period doubling cascade is investigated. The growth of the stochastic sensitivity under transition to chaos is shown.
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41

Xu, Chaoqun, Sanling Yuan, and Tonghua Zhang. "Stochastic Sensitivity Analysis for a Competitive Turbidostat Model with Inhibitory Nutrients." International Journal of Bifurcation and Chaos 26, no. 10 (September 2016): 1650173. http://dx.doi.org/10.1142/s021812741650173x.

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A stochastic model of turbidostat in which two microorganism species compete for an inhibitory growth-limiting nutrient is considered. In the deterministic case, the model has rich dynamics: a coexistence equilibrium and the washout equilibrium can be simultaneously stable, and a stable limit cycle may exist. In the stochastic case, a phenomenon of noise-induced extinction occurs. Namely, the stochastic trajectory near the deterministic coexistence equilibrium will tend to the washout equilibrium. Based on the stochastic sensitivity function technique, in this paper, we construct the confidence ellipse and then estimate the critical value of the intensity for noise generating a transition from coexistence to extinction. We also construct the confidence band to find the configurational arrangement of the stochastic cycle.
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42

Kolinichenko, A. P., and L. B. Ryashko. "Analysis of stochastic sensitivity of Turing patterns in distributed reaction-diffusion systems." Izvestiya Instituta Matematiki i Informatiki Udmurtskogo Gosudarstvennogo Universiteta 55 (May 2020): 155–63. http://dx.doi.org/10.35634/2226-3594-2020-55-10.

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In this paper, a distributed stochastic Brusselator model with diffusion is studied. We show that a variety of stable spatially heterogeneous patterns is generated in the Turing instability zone. The effect of random noise on the stochastic dynamics near these patterns is analysed by direct numerical simulation. Noise-induced transitions between coexisting patterns are studied. A stochastic sensitivity of the pattern is quantified as the mean-square deviation from the initial unforced pattern. We show that the stochastic sensitivity is spatially non-homogeneous and significantly differs for coexisting patterns. A dependence of the stochastic sensitivity on the variation of diffusion coefficients and intensity of noise is discussed.
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43

Bashkirtseva, Irina, Guanrong Chen, and Lev Ryashko. "Stochastic equilibria control and chaos suppression for 3D systems via stochastic sensitivity synthesis." Communications in Nonlinear Science and Numerical Simulation 17, no. 8 (August 2012): 3381–89. http://dx.doi.org/10.1016/j.cnsns.2011.12.004.

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44

Wei, X. F., and S. N. Patnaik. "Application of Stochastic Sensitivity Analysis to Integrated Force Method." International Journal of Stochastic Analysis 2012 (June 7, 2012): 1–14. http://dx.doi.org/10.1155/2012/249201.

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As a new formulation in structural analysis, Integrated Force Method has been successfully applied to many structures for civil, mechanical, and aerospace engineering due to the accurate estimate of forces in computation. Right now, it is being further extended to the probabilistic domain. For the assessment of uncertainty effect in system optimization and identification, the probabilistic sensitivity analysis of IFM was further investigated in this study. A set of stochastic sensitivity analysis formulation of Integrated Force Method was developed using the perturbation method. Numerical examples are presented to illustrate its application. Its efficiency and accuracy were also substantiated with direct Monte Carlo simulations and the reliability-based sensitivity method. The numerical algorithm was shown to be readily adaptable to the existing program since the models of stochastic finite element and stochastic design sensitivity are almost identical.
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45

Cao, Xi-Ren. "Stochastic Learning and Optimization - A Sensitivity-Based Approach." IFAC Proceedings Volumes 41, no. 2 (2008): 3480–92. http://dx.doi.org/10.3182/20080706-5-kr-1001.00589.

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46

Ugrinovskii, Valery A. "Risk-sensitivity Conditions for Stochastic Uncertain Model Validation." IFAC Proceedings Volumes 41, no. 2 (2008): 15309–14. http://dx.doi.org/10.3182/20080706-5-kr-1001.02589.

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47

Pedersen, Lars, and Christian Frier. "Sensitivity of footbridge vibrations to stochastic walking parameters." Journal of Sound and Vibration 329, no. 13 (June 2010): 2683–701. http://dx.doi.org/10.1016/j.jsv.2009.12.022.

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48

Bashkirtseva, Irina. "Stochastic sensitivity of systems driven by colored noise." Physica A: Statistical Mechanics and its Applications 505 (September 2018): 729–36. http://dx.doi.org/10.1016/j.physa.2018.03.095.

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49

Kim, Kyung Hyuk, and Herbert M. Sauro. "Sensitivity summation theorems for stochastic biochemical reaction systems." Mathematical Biosciences 226, no. 2 (August 2010): 109–19. http://dx.doi.org/10.1016/j.mbs.2010.04.004.

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50

Paulsson, J., O. G. Berg, and M. Ehrenberg. "Stochastic focusing: Fluctuation-enhanced sensitivity of intracellular regulation." Proceedings of the National Academy of Sciences 97, no. 13 (June 13, 2000): 7148–53. http://dx.doi.org/10.1073/pnas.110057697.

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