Готові списки джерел за темами / Stochastic parameters modelling

Добірка наукової літератури з теми "Stochastic parameters modelling"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Stochastic parameters modelling"

1

Beutler, G., A. Jäggi, U. Hugentobler, and L. Mervart. "Efficient satellite orbit modelling using pseudo-stochastic parameters." Journal of Geodesy 80, no. 7 (August 24, 2006): 353–72. http://dx.doi.org/10.1007/s00190-006-0072-6.

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2

Borzunov, S. V., M. E. Semenov, N. I. Sel’vesyuk, and P. A. Meleshenko. "Hysteretic Converters with Stochastic Parameters." Mathematical Models and Computer Simulations 12, no. 2 (March 2020): 164–75. http://dx.doi.org/10.1134/s2070048220020040.

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3

Ellam, L., M. Girolami, G. A. Pavliotis, and A. Wilson. "Stochastic modelling of urban structure." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2213 (May 2018): 20170700. http://dx.doi.org/10.1098/rspa.2017.0700.

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The building of mathematical and computer models of cities has a long history. The core elements are models of flows (spatial interaction) and the dynamics of structural evolution. In this article, we develop a stochastic model of urban structure to formally account for uncertainty arising from less predictable events. Standard practice has been to calibrate the spatial interaction models independently and to explore the dynamics through simulation. We present two significant results that will be transformative for both elements. First, we represent the structural variables through a single potential function and develop stochastic differential equations to model the evolution. Second, we show that the parameters of the spatial interaction model can be estimated from the structure alone, independently of flow data, using the Bayesian inferential framework. The posterior distribution is doubly intractable and poses significant computational challenges that we overcome using Markov chain Monte Carlo methods. We demonstrate our methodology with a case study on the London, UK, retail system.
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4

Marano, Giuseppe Carlo, Mariantonietta Morga, and Sara Sgobba. "Parameters Identification of Stochastic Nonstationary Process Used in Earthquake Modelling." International Journal of Geosciences 04, no. 02 (2013): 290–301. http://dx.doi.org/10.4236/ijg.2013.42027.

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5

Intsiful, Joseph, and Harald Kunstmann. "Upscaling of Land-Surface Parameters Through Inverse Stochastic SVAT-Modelling." Boundary-Layer Meteorology 129, no. 1 (September 13, 2008): 137–58. http://dx.doi.org/10.1007/s10546-008-9303-0.

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6

Dørum, C., O. S. Hopperstad, T. Berstad, and D. Dispinar. "Numerical modelling of magnesium die-castings using stochastic fracture parameters." Engineering Fracture Mechanics 76, no. 14 (September 2009): 2232–48. http://dx.doi.org/10.1016/j.engfracmech.2009.07.001.

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7

Frederiksen, Jorgen S., Terence J. O'Kane, and Meelis J. Zidikheri. "Subgrid modelling for geophysical flows." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1982 (January 13, 2013): 20120166. http://dx.doi.org/10.1098/rsta.2012.0166.

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Recently developed closure-based and stochastic model approaches to subgrid-scale modelling of eddy interactions are reviewed. It is shown how statistical dynamical closure models can be used to self-consistently calculate the eddy damping and stochastic backscatter parameters, required in large eddy simulations (LESs), from higher resolution simulations. A closely related direct stochastic modelling scheme that is more generally applicable to complex models is then described and applied to LESs of quasi-geostrophic turbulence of the atmosphere and oceans. The fundamental differences between atmospheric and oceanic LESs, which are related to the difference in the deformation scales in the two classes of flows, are discussed. It is noted that a stochastic approach may be crucial when baroclinic instability is inadequately resolved. Finally, inhomogeneous closure theory is applied to the complex problem of flow over topography; it is shown that it can be used to understand the successes and limitations of currently used heuristic schemes and to provide a basis for further developments in the future.
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Romansky, Radi. "Mathematical Modelling and Study of Stochastic Parameters of Computer Data Processing." Mathematics 9, no. 18 (September 12, 2021): 2240. http://dx.doi.org/10.3390/math9182240.

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Анотація:
The main goal of dispatching strategies is to minimize the total time for processing tasks at maximum performance of the computer system, which requires strict regulation of the workload of the processing units. To achieve this, it is necessary to conduct a preliminary study of the applied model for planning. The purpose of this article is to present an approach for automating the investigation and optimization of processes in a computer environment for task planning and processing. A stochastic input flow of incoming tasks for processing is considered and mathematical formalization of some probabilistic characteristics related to the complexity of its servicing has been made. On this basis, a software module by using program language APL2 has been developed to conduct experiments for analytical study and obtaining estimates of stochastic parameters of computer processing and dispatching. The proposed model is part of a generalized environment for program investigation of the computer processing organization and expands its field of application with additional research possibilities.
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Gassmann, H. I., and G. Infanger. "Modelling history-dependent parameters in the SMPS format for stochastic programming." IMA Journal of Management Mathematics 19, no. 1 (February 2, 2007): 87–97. http://dx.doi.org/10.1093/imaman/dpm006.

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ARTEMEV, S. S., and M. A. YAKUNIN. "Estimation of the parameters in stochastic differential equations." Russian Journal of Numerical Analysis and Mathematical Modelling 12, no. 1 (1997): 1–12. http://dx.doi.org/10.1515/rnam.1997.12.1.1.

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Більше джерел

Дисертації з теми "Stochastic parameters modelling"

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Kucharska, Magdalena, and Jolanta Pielaszkiewicz. "NIG distribution in modelling stock returns with assumption about stochastic volatility : Estimation of parameters and application to VaR and ETL." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-2874.

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We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatility. We consider different methods of parametrization of returns and following the paper of Lindberg, [21] we

assume that the volatility is a linear function of the number of trades. In addition to the Lindberg’s paper, we suggest daily stock volumes and amounts as alternative measures of the volatility.

As an application of the models, we perform Value-at-Risk and Expected Tail Loss predictions by the Lindberg’s volatility model and by our own suggested model. These applications are new and not described in the

literature. For better understanding of our caluclations, programmes and simulations, basic informations and properties about the Normal Inverse Gaussian and Inverse Gaussian distributions are provided. Practical applications of the models are implemented on the Nasdaq-OMX, where we have calculated Value-at-Risk and Expected Tail Loss

for the Ericsson B stock data during the period 1999 to 2004.

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2

Kucharska, Magdalena, and Jolanta Maria Pielaszkiewicz. "NIG distribution in modelling stock returns with assumption about stochastic volatility : Estimation of parameters and application to VaR and ETL." Thesis, Halmstad University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-58180.

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Анотація:
We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatility. We consider different methods of parametrization of returns and following the paper of Lindberg, [21] we assume that the volatility is a linear function of the number of trades. In addition to the Lindberg’s paper, we suggest daily stock volumes and amounts as alternative measures of the volatility. As an application of the models, we perform Value-at-Risk and Expected Tail Loss predictions by the Lindberg’s volatility model and by our own suggested model. These applications are new and not described in the literature. For better understanding of our caluclations, programmes and simulations, basic informations and properties about the Normal Inverse Gaussian and Inverse Gaussian distributions are provided. Practical applications of the models are implemented on the Nasdaq-OMX, where we have calculated Value-at-Risk and Expected Tail Loss for the Ericsson B stock data during the period 1999 to 2004.
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3

Mesbahi, Abdessamad. "Deterministic and Stochastic Economic Modeling of Hybrid Power Supply System with Photovoltaic Generators." Master's thesis, КПІ ім. Ігоря Сікорського, 2021. https://ela.kpi.ua/handle/123456789/42555.

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Relevance of research. Due to the rapid deployment of the non-dispatchable (intermittent) generation sources in the smart grid, such as integration of the photovoltaic power plants and wind turbines in the distribution systems; this caused a problem of the uncertainty increase of simulation results for decision-making for power supply systems, these uncertainties of power systems are getting more and more notice. At the same time, the classical power systems models cannot give accurate simulation results. Wherein; it became necessary to define new models to represent the specific parameters of power system. wherein; this research reveals to the benefits of using probabilistic mathematical approaches to define and calculate the specific economic parameters, as well as the technical parameters for power supply system with the integration renewable energy generators, which are characterising by randomness and uncertainty due to the high penetration to the renewables. Monte Carlo Method, and Point Estimation Method are used to handle the uncertainties of renewable energy resources. The standard functions to represent the stochastic parameters of the model are analyzed with the use of three-point estimation technique for the distribution functions of their probable values. A synthetic skewed probability density function was analytically constructed basing on the standard normal distribution, which is suitable for analytic representation of the predicted and/or statistical random sampling of the uncertain model parameters of energy system with renewables, and analytical expressions were obtained to compute the moments of proposed synthetic probability function. Relationship of work with scientific programs, plans, themes. is to demonstrate the possibility of describing the input parameters of the simulation Deterministic and Stochastic Modeling by probability Density Functions by the use of three-point approximation techniques and to obtain analytical expressions for the characteristics of such distributions, suitable for non-iterative (as opposed to Monte Carlo Method) probabilistic method applications, namely the Point Estimation Method. Purpose and tasks of the research. Increasing the simulation accuracy results for estimation economic and technical parameters characterising photovoltaic power plant based on based on the life cycle model; as well as development of different algorithms based on deterministic and stochastic modeling of power system with non-dispatchable sources and minimize the computation time. Object of research. Processes of determining the estimated technical and economic parameters characterising a photovoltaic power plant located in Ukraine basing on stochastic modeling. Subject of research. Use of the Monte Carlo Method and Point Estimation Method to estimate the various economic and technical information characteristic of alternative power plants in order to obtain accurate simulation results. Practical value of the results. Practical techniques of the three-point approximation are used to construct the probability density function of the model uncertain (stochastic) parameter, which dominantly influences the modeling result: an event occurrence probability, the result attainability, whatsoever. This technique is an effective tool for the practical evaluating of an uncertain value of a technological or economic factor of material and/or economic object, and widely used for overall Levelized Energy Cost (LCOE – LEC) which is directly or indirectly engaged into analytic representation of the power systel model. Usually, the model of a kind is designed to solve technical and/or economic problem by means of Deterministic and Stochastic Modeling. Scientific novelty of the obtained results is the development of algorithms and mathematical solutions using a probabilistic approach basing Point Estimation Method instead of Monte Carlo Melthod to obtain more accurate estimation simulation results, as well as to obtain computational results in less time for useful decision-making in alternative power plant projects.
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4

Chen, Liang. "Small population bias and sampling effects in stochastic mortality modelling." Thesis, Heriot-Watt University, 2017. http://hdl.handle.net/10399/3372.

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Анотація:
Pension schemes are facing more difficulties on matching their underlying liabilities with assets, mainly due to faster mortality improvements for their underlying populations, better environments and medical treatments and historically low interest rates. Given most of the pension schemes are relatively much smaller than the national population, modelling and forecasting the small populations' longevity risk become urgent tasks for both the industrial practitioners and academic researchers. This thesis starts with a systematic analysis on the influence of population size on the uncertainties of mortality estimates and forecasts with a stochastic mortality model, based on a parametric bootstrap methodology with England and Wales males as our benchmark population. The population size has significant effect on the uncertainty of mortality estimates and forecasts. The volatilities of small populations are over-estimated by the maximum likelihood estimators. A Bayesian model is developed to improve the estimation of the volatilities and the predictions of mortality rates for the small populations by employing the information of larger population with informative prior distributions. The new model is validated with the simulated small death scenarios. The Bayesian methodologies generate smoothed estimations for the mortality rates. Moreover, a methodology is introduced to use the information of large population for obtaining unbiased volatilities estimations given the underlying prior settings. At last, an empirical study is carried out based on the Scotland mortality dataset.
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5

Lück, Alexander Tobias [Verfasser]. "Stochastic spatial modelling of DNA methylation patterns and moment-based parameter estimation / Alexander Tobias Lück." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1219068659/34.

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6

Tran, The Truyen. "On conditional random fields: applications, feature selection, parameter estimation and hierarchical modelling." Curtin University of Technology, Dept. of Computing, 2008. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=18614.

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Анотація:
There has been a growing interest in stochastic modelling and learning with complex data, whose elements are structured and interdependent. One of the most successful methods to model data dependencies is graphical models, which is a combination of graph theory and probability theory. This thesis focuses on a special type of graphical models known as Conditional Random Fields (CRFs) (Lafferty et al., 2001), in which the output state spaces, when conditioned on some observational input data, are represented by undirected graphical models. The contributions of thesis involve both (a) broadening the current applicability of CRFs in the real world and (b) deepening the understanding of theoretical aspects of CRFs. On the application side, we empirically investigate the applications of CRFs in two real world settings. The first application is on a novel domain of Vietnamese accent restoration, in which we need to restore accents of an accent-less Vietnamese sentence. Experiments on half a million sentences of news articles show that the CRF-based approach is highly accurate. In the second application, we develop a new CRF-based movie recommendation system called Preference Network (PN). The PN jointly integrates various sources of domain knowledge into a large and densely connected Markov network. We obtained competitive results against well-established methods in the recommendation field.
On the theory side, the thesis addresses three important theoretical issues of CRFs: feature selection, parameter estimation and modelling recursive sequential data. These issues are all addressed under a general setting of partial supervision in that training labels are not fully available. For feature selection, we introduce a novel learning algorithm called AdaBoost.CRF that incrementally selects features out of a large feature pool as learning proceeds. AdaBoost.CRF is an extension of the standard boosting methodology to structured and partially observed data. We demonstrate that the AdaBoost.CRF is able to eliminate irrelevant features and as a result, returns a very compact feature set without significant loss of accuracy. Parameter estimation of CRFs is generally intractable in arbitrary network structures. This thesis contributes to this area by proposing a learning method called AdaBoost.MRF (which stands for AdaBoosted Markov Random Forests). As learning proceeds AdaBoost.MRF incrementally builds a tree ensemble (a forest) that cover the original network by selecting the best spanning tree at a time. As a result, we can approximately learn many rich classes of CRFs in linear time. The third theoretical work is on modelling recursive, sequential data in that each level of resolution is a Markov sequence, where each state in the sequence is also a Markov sequence at the finer grain. One of the key contributions of this thesis is Hierarchical Conditional Random Fields (HCRF), which is an extension to the currently popular sequential CRF and the recent semi-Markov CRF (Sarawagi and Cohen, 2004). Unlike previous CRF work, the HCRF does not assume any fixed graphical structures.
Rather, it treats structure as an uncertain aspect and it can estimate the structure automatically from the data. The HCRF is motivated by Hierarchical Hidden Markov Model (HHMM) (Fine et al., 1998). Importantly, the thesis shows that the HHMM is a special case of HCRF with slight modification, and the semi-Markov CRF is essentially a flat version of the HCRF. Central to our contribution in HCRF is a polynomial-time algorithm based on the Asymmetric Inside Outside (AIO) family developed in (Bui et al., 2004) for learning and inference. Another important contribution is to extend the AIO family to address learning with missing data and inference under partially observed labels. We also derive methods to deal with practical concerns associated with the AIO family, including numerical overflow and cubic-time complexity. Finally, we demonstrate good performance of HCRF against rivals on two applications: indoor video surveillance and noun-phrase chunking.
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Gallet, Emmanuelle. "Techniques de model-checking pour l’inférence de paramètres et l’analyse de réseaux biologiques." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC035/document.

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Анотація:
Dans ce mémoire, nous présentons l’utilisation de techniques de model-checking pour l’inférence de paramètres de réseaux de régulation génétique (GRN) et l’analyse formelle d’une voie de signalisation. Le coeur du mémoire est décrit dans la première partie, dans laquelle nous proposons une approche pour inférer les paramètres biologiques régissant les dynamiques de modèles discrets de GRN. Les GRN sont encodés sous la forme d’un méta-modèle, appelé GRN paramétré, de telle façon qu’une instance de paramètres définit un modèle discret du GRN initial. Sous réserve que les propriétés biologiques d’intérêt s’expriment sous la forme de formules LTL, les techniques de model-checking LTL sont combinées à celles d’exécution symbolique et de résolution de contraintes afin de sélectionner les modèles satisfaisant ces propriétés. L’enjeu est de contourner l’explosion combinatoire en terme de taille et de nombre de modèles discrets. Nous avons implémenté notre méthode en Java, dans un outil appelé SPuTNIk. La seconde partie décrit une collaboration avec des neuropédiatres, qui ont pour objectif de comprendre l’apparition du phénotype protecteur ou toxique des microglies (un type de macrophage du cerveau) chez les prématurés. Cette partie exploite un autre versant du model-checking, celui du modelchecking statistique, afin d’étudier un type de réseau biologique particulier : la voie de signalisation Wnt/β-caténine, qui permet la transmission d’un signal de l’extérieur à l’intérieur des cellules via une cascade de réactions biochimiques. Nous présentons ici l’apport du model-checker stochastique COSMOS, utilisant la logique stochastique à automate hybride (HASL), un formalisme très expressif nous permettant une analyse formelle sophistiquée des dynamiques de la voie Wnt/β-caténine, modélisée sous la forme d’un processus stochastique à événements discrets
In this thesis, we present the use of model checking techniques for inference of parameters of Gene Regulatory Networks (GRNs) and formal analysis of a signalling pathway. In the first and main part, we provide an approach to infer biological parameters governing the dynamics of discrete models of GRNs. GRNs are encoded in the form of a meta-model, called Parametric GRN, such that a parameter instance defines a discrete model of the original GRN. Provided that targeted biological properties are expressed in the form of LTL formulas, LTL model-checking techniques are combined with symbolic execution and constraint solving techniques to select discrete models satisfying these properties. The challenge is to prevent combinatorial explosion in terms of size and number of discrete models. Our method is implemented in Java, in a tool called SPuTNIk. The second part describes a work performed in collaboration with child neurologists, who aim to understand the occurrence of toxic or protective phenotype of microglia (a type of macrophage in the brain) in the case of preemies. We use an other type of model-checking, the statistical model-checking, to study a particular type of biological network: the Wnt/β- catenin pathway that transmits an external signal into the cells via a cascade of biochemical reactions. Here we present the benefit of the stochastic model checker COSMOS, using the Hybrid Automata Stochastic Logic (HASL), that is an very expressive formalism allowing a sophisticated formal analysis of the dynamics of the Wnt/β-catenin pathway, modelled as a discrete event stochastic process
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8

Abhinav, S. "Stochastic Modelling of Vehicle-Structure Interactions : Dynamic State And Parameter Estimation, And Global Response Sensitivity Analysis." Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2736.

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Анотація:
The analysis of vehicle-structure interaction systems plays a significant role in the design and maintenance of bridges. In recent years, the assessment of the health of existing bridges and the design of new ones has gained significance, in part due to the progress made in the development of faster moving locomotives, the desire for lighter bridges, and the imposition of performance criteria against rare events such as occurrence of earthquakes and fire. A probabilistic analysis would address these issues, and also assist in determination of reliability and in estimating the remaining life of the structure. In this thesis, we aim to develop tools for the probabilistic analysis techniques of state estimation, parameter identification and global response sensitivity analysis of vehicle-structure interaction systems, which are also applicable to the broader class of structural dynamical systems. The thesis is composed of six chapters and three appendices. The contents of these chapters and the appendices are described in brief in the following paragraphs. In chapter 1, we introduce the problem of probabilistic analysis of vehicle-structure interactions. The introduction is organized in three parts, dealing separately with issues of forward problems, inverse problems, and global response sensitivity analysis. We begin with an overview of the modelling and analysis of vehicle-structure interaction systems, including the application of spatial substructuring and mesh partitioning schemes. Following this, we describe Bayesian techniques for state and parameter estimation for the general class of state-space models of dynamical systems, including the application of the Kalman filter and particle filters for state estimation, MCMC sampling based filters for parameter identification, and the extended Kalman filter, the unscented Kalman filter and the ensemble Kalman filter for the problem of combined state and parameter identification. In this context, we present the Rao-Blackwellization method which leads to variance reduction in particle filtering. Finally, we present the techniques of global response sensitivity analysis, including Sobol’s analysis and distance-based measures of sensitivity indices. We provide an outline and a review of literature on each of these topics. In our review of literature, we identify the difficulties encountered when adopting these tools to problems involving vehicle-structure interaction systems, and corresponding to these issues, we identify some open problems for research. These problems are addressed in chapters 2, 3, 4 and 5. In chapter 2, we study the application of finite element modelling, combined with numerical solutions of governing stochastic differential equations, to analyse instrumented nonlinear moving vehicle-structure systems. The focus of the chapter is on achieving computational efficiency by deploying, within a single modeling framework, three sub structuring schemes with different methodological moorings. The schemes considered include spatial substructuring schemes (involving free-interface coupling methods), a spatial mesh partitioning scheme for governing stochastic differential equations (involving the use of a predictor corrector method with implicit integration schemes for linear regions and explicit schemes for local nonlinear regions), and application of the Rao-Blackwellization scheme (which permits the use of Kalman’s filtering for linear substructures and Monte Carlo filters for nonlinear substructures). The main effort in this work is expended on combining these schemes with provisions for interfacing of the substructures by taking into account the relative motion of the vehicle and the supporting structure. The problem is formulated with reference to an archetypal beam and multi-degrees of freedom moving oscillator with spatially localized nonlinear characteristics. The study takes into account imperfections in mathematical modelling, guide way unevenness, and measurement noise. The numerical results demonstrate notable reduction in computational effort achieved on account of introduction of the substructuring schemes. In chapter 3, we address the issue of identification of system parameters of structural systems using dynamical measurement data. When Markov chain Monte Carlo (MCMC) samplers are used in problems of system parameter identification, one would face computational difficulties in dealing with large amount of measurement data and (or) low levels of measurement noise. Such exigencies are likely to occur in problems of parameter identification in dynamical systems when amount of vibratory measurement data and number of parameters to be identified could be large. In such cases, the posterior probability density function of the system parameters tends to have regions of narrow supports and a finite length MCMC chain is unlikely to cover pertinent regions. In this chapter, strategies are proposed based on modification of measurement equations and subsequent corrections, to alleviate this difficulty. This involves artificial enhancement of measurement noise, assimilation of transformed packets of measurements, and a global iteration strategy to improve the choice of prior models. Illustrative examples include a laboratory study on a beam-moving trolley system. In chapter 4, we consider the combined estimation of the system states and parameters of vehicle-structure interaction systems. To this end, we formulate a framework which uses MCMC sampling for parameter estimation and particle filtering for state estimation. In chapters 2 and 3, we described the computational issues faced when adopting these techniques individually. When used together, we come across both sets of issues, and find the complexity of the estimation problem is greatly increased. In this chapter, we address the computational issues by adopting the sub structuring techniques proposed in chapter 2, and the parameter identification method based on modified measurement models presented in chapter 3. The proposed method is illustrated on a computational study on a beam-moving oscillator system with localized nonlinearities, as well as on a laboratory study on a beam-moving trolley system. In chapter 5, we present global response sensitivity indices for structural dynamical systems with random system parameters excited by multiple random excitations. Two new procedures for evaluating global response sensitivity measures with respect to the excitation components are proposed. The first procedure is valid for stationary response of linear systems under stationary random excitations and is based on the notion of Hellinger’s metric of distance between two power spectral density functions. The second procedure is more generally valid and is based on the l2 norm based distance measure between two probability density functions. Specific cases which admit exact solutions are presented and solution procedures based on Monte Carlo simulations for more general class of problems are outlined. The applicability of the proposed procedures to the case of random system parameters is demonstrated using suitable illustrations. Illustrations include studies on a parametrically excited linear system and a nonlinear random vibration problem involving moving oscillator-beam system that considers excitations due to random support motions and guide-way unevenness. In chapter 6 we summarize the contributions made in chapters 2, 3, 4, and 5, and on the basis of these studies, present a few problems for future research. In addition to these chapters, three appendices are included in this thesis. Appendices A and B correspond to chapter 3. In appendix A, we study the effect on the nature of the posterior probability density functions of large measurement data set and small measurement noise. Appendix B illustrates the MCMC sampling based parameter estimation procedure of chapter 3 using a laboratory study on a bending–torsion coupled, geometrically non-linear building frame under earthquake support motion. In appendix C, we present Ito-Taylor time discretization schemes for stochastic delay differential equations found in chapter 5.
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Varziri, M. Saeed. "Parameter estimation in nonlinear continuous-time dynamic models with modelling errors and process disturbances." Thesis, 2008. http://hdl.handle.net/1974/1248.

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Анотація:
Model-based control and process optimization technologies are becoming more commonly used by chemical engineers. These algorithms rely on fundamental or empirical models that are frequently described by systems of differential equations with unknown parameters. It is, therefore, very important for modellers of chemical engineering processes to have access to reliable and efficient tools for parameter estimation in dynamic models. The purpose of this thesis is to develop an efficient and easy-to-use parameter estimation algorithm that can address difficulties that frequently arise when estimating parameters in nonlinear continuous-time dynamic models of industrial processes. The proposed algorithm has desirable numerical stability properties that stem from using piece-wise polynomial discretization schemes to transform the model differential equations into a set of algebraic equations. Consequently, parameters can be estimated by solving a nonlinear programming problem without requiring repeated numerical integration of the differential equations. Possible modelling discrepancies and process disturbances are accounted for in the proposed algorithm, and estimates of the process disturbance intensities can be obtained along with estimates of model parameters and states. Theoretical approximate confidence interval expressions for the parameters are developed. Through a practical two-phase nylon reactor example, as well as several simulation studies using stirred tank reactors, it is shown that the proposed parameter estimation algorithm can address difficulties such as: different types of measured responses with different levels of measurement noise, measurements taken at irregularly-spaced sampling times, unknown initial conditions for some state variables, unmeasured state variables, and unknown disturbances that enter the process and influence its future behaviour.
Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2008-06-20 16:34:44.586
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Книги з теми "Stochastic parameters modelling"

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Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.

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This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data for the year 2007 for three market indices: the Standard and Poor’s S&P500, the NASDAQ NDX100 and the financial equity index called XLF. Three models of financial time series are estimated: a model with stochastic volatility, a model with stochastic volatility that also incorporates jumps in volatility, and a Garch model.
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Частини книг з теми "Stochastic parameters modelling"

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Meshram, Deodas T., V. T. Jadhav, S. D. Gorantiwar, and Ram Chandra. "Modeling of Weather Parameters Using Stochastic Methods." In Climate Change Modelling, Planning and Policy for Agriculture, 67–77. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2157-9_8.

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Al-Ani, Tarik, and Yskander Hamam. "Parameters identification of a time-varying stochastic dynamic systems using Viterbi algorithm." In System Modelling and Optimization, 567–73. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34897-1_69.

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Purutçuoğlu, Vilda. "Stochastic Modelling of Biochemical Networks and Inference of Model Parameters." In Springer Proceedings in Mathematics & Statistics, 369–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74086-7_18.

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Ma, Yue, Jiaxin Liu, and Xuzhao Hou. "Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation." In Lecture Notes in Electrical Engineering, 349–61. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6320-8_37.

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Vidhya, C., and P. Balasubramaniam. "Stability of Uncertain Reaction-Diffusion Stochastic BAM Neural Networks with Mixed Delays and Markovian Jumping Parameters." In Mathematical Modelling and Scientific Computation, 283–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28926-2_30.

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He, Zhongjie, Hua Li, and Karl Erik Birgersson. "Integrated Stochastic and Deterministic Sensitivity Analysis: Correlating Variability of Design Parameters with Cell and Stack Performance." In Reduced Modelling of Planar Fuel Cells, 227–69. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42646-4_6.

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Wanduku, Divine, C. Newman, O. Jegede, and B. Oluyede. "Modeling the Stochastic Dynamics of Influenza Epidemics with Vaccination Control, and the Maximum Likelihood Estimation of Model Parameters." In Mathematical Modelling in Health, Social and Applied Sciences, 23–72. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2286-4_2.

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Mohanty, Ashrumochan, Satish Chandra Bhuyan, and Sangeeta Kumari. "Impact of Different Parameters in the Development of Operating Policies of a Reservoir Using Stochastic Dynamic Modelling Technique." In Lecture Notes in Civil Engineering, 353–69. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4629-4_25.

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Liptser, Robert S., and Albert N. Shiryaev. "Parameter Estimation and Testing of Statistical Hypotheses for Diffusion-Type Processes." In Stochastic Modelling and Applied Probability, 219–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-10028-8_7.

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Stettner, L. "Adaptive control of semilinear stochastic evolution equations." In Modelling and Optimization of Distributed Parameter Systems Applications to engineering, 278–86. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34922-0_29.

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Тези доповідей конференцій з теми "Stochastic parameters modelling"

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Kumar, Rahul, Sayan Gupta, and Shaikh Faruque Ali. "Stochastic Modelling and Analysis of Rotating Bladed Discs." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-16212.

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Abstract This study focusses on probabilistic modelling of the bladed disc system and numerical estimation of the distributions of the response quantities of the system. Stochastic finite element model of the system consisting of all the assemblies and the hub is developed and reported. The spatial inhomogeneity of mistuned structures is modelled as non-Gaussian random field. Experimentally, the system parameters can be measured at the specified locations of the bladed disk structure. In this analysis, a synthetic data is generated which represent this measured data set. Further, Nataf transformation is implemented to each component of the data set to get the polynomial chaos expansion framework of the system parameters. Since, the random field of the system parameter is approximated as correlated random variables, Spearman’s rank correlation coefficient is used in this manuscript to obtain that correlation among the random parameters across the domain. The approximated probability density function obtained through the aforementioned methodology is compared with the target probability density function of the parameter using Kullback–Liebler (KL) entropy as a metric. Also, the same KL entropy is used as a metric to check the convergence of polynomial chaos terms in the expansion. Next, the proposed polynomial chaos method is integrated with commercial finite element software to quantify the propagation of randomness associated with system parameters into the response quantities. Subsequently, the statistical processing helps in estimating the probabilistic measure of the required response quantities. The results obtained through the conventional Monte Carlo (MC) simulations have been used as the benchmark to compare the response characteristics obtained through the proposed algorithm.
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Gorobetz, M., and A. Levchenkov. "Modelling Of Stochastic Parameters For Control Of City Electric Transport Systems Using Evolutionary Algorithm." In 22nd Conference on Modelling and Simulation. ECMS, 2008. http://dx.doi.org/10.7148/2008-0207.

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PITZ, EMIL J., SEAN ROONEY, and KISHORE V. POCHIRAJU. "Stochastic Modelling of Additively Manufactured Structures Using a Neural Network for Identification of Random Field Parameters." In American Society for Composites 2020. Lancaster, PA: DEStech Publications, Inc., 2020. http://dx.doi.org/10.12783/asc35/34974.

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Vanem, Erik. "Stochastic Models for Long-Term Prediction of Extreme Waves: A Literature Survey." In ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/omae2010-20076.

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This paper presents a literature survey on time-dependent statistical modelling of extreme waves. The focus is twofold: on statistical modelling of extreme waves and time-dependent statistical modelling. The first part will consist of a thorough literature review of statistical modelling of extreme waves and wave parameters. The second part will focus on statistical modelling of time- and space-dependent variables in a more general sense, and will focus on the methodology and models used also in other relevant application areas. It was found that limited effort has been put on developing statistical models for waves incorporating spatial and long-term temporal variability and it is suggested that model improvements could be achieved by adopting approaches from other application areas. Finally, a review of projections of future extreme wave climate is presented.
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Wegener, Konrad, Guilherme E. Vargas, Friedrich Kuster, Fa´bio W. Pinto, and Thomas Schnider. "Modelling of Hard Broaching." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59454.

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Hard broaching is a finishing process to correct geometrical deviations of internal profiles on hardened workpieces, typically using a hardened steel body overlaid with a single layer of metal-bonded diamond grits. The tool essentially has the opposite contour as the workpiece. The process is characterized by an oscillating movement of the broaching tool, while the tool is pushed sequentially deeper into the workpiece. During the retraction phase the chips are removed from the chip spaces. The tool consists of a roughing part and a finishing part to increase the surface quality and reduce the tolerances. For gaining a deeper understanding of the process and for its optimization a stochastic tool model is introduced, which takes into account the differing shape, size, orientation and position of the single grains. Specifications about the tool geometry and the diamond coating as well as process parameters are used as input. The model is capable to predict the active grains, the respective cutting areas, cutting forces and surface roughness of such a virtual image of the broaching tool, which is thus capable to be used to layout and optimize the shape, layer and process strategy of hard broaching. It further allows analyzing the effects of the feed per stroke on the process in dependence of different process and tool parameters. By the modeling a strategy for process optimization is derived. The influences of the optimization strategy on the process are presented and discussed.
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Pukl, Radomír, David Lehký, and Drahomír Novák. "Towards nonlinear reliability assessment of concrete transport structures." In IABSE Conference, Kuala Lumpur 2018: Engineering the Developing World. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2018. http://dx.doi.org/10.2749/kualalumpur.2018.0330.

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<p>Novel technique combining non-linear finite element analysis of the structural model with advanced stochastic simulation methods for realistic computer simulation and reliability assessment of civil engineering structures is presented. Elite non-linear material models are used for modelling of the structural materials within an advanced finite element computer simulation. Material properties and corresponding model parameters including their randomness and uncertainties are represented as random variables or random fields in the stochastic simulation using stratified Latin Hypercube Sampling and Simulated Annealing methods. Probabilistic evaluation of the numerical results enables to assess stochastic parameters of the model response, structural resistance, failure probability, safety index and structural reliability.</p>
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Pozo Dominguez, Alejandro, Nicholas Hills, and Simao Marques. "Sensitivity Analysis and Uncertainty Quantification for Rim Seal Ingestion With 1-D Network Models." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15218.

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Abstract Accurate prediction of turbine rim seal ingestion remains a challenge, even sophisticated unsteady computational models have had limited success due to the complexity and uncertainties present in this type of problems. For this reason, it is of interest to perform stochastic analysis taking into account the variability in the input parameters as well as uncertainties and assumptions associated with a given model of choice. This study focuses on a Secondary Air System (SAS) of a gas turbine, considering a generic cavity in a high pressure turbine (HPT) in which hot gas ingestion occurs and uncertainty in geometrical, operational and modelling parameters is present. Several statistical methods are applied to a 1D gas network model in order to evaluate the impact of the tolerances of the main geometrical parameters in the output, followed by a broader analysis including other operational and modelling variables. Results indicate that most influential parameters are the sealant mass flow rate, a modelling constant and the minimum gap of the wheelspace cavity.
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Mercatali, Luigi, Yousef Alzaben, and Victor Hugo Sanchez Espinoza. "Propagation of Nuclear Data Uncertainties in PWR Pin-Cell Burnup Calculations via Stochastic Sampling." In 2018 26th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icone26-81711.

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In recent years there has been an increasing demand from nuclear research, industry, safety, and regulation bodies for best estimate predictions of Light Water Reactors (LWR’s) performances to be provided with their confidence bounds. From a neutronic point of view, among the different sources of uncertainty the main challenge is represented by the one related to the accuracy of the nuclear data libraries used in the transport calculations. The assessment of nuclear data uncertainties and their impact on the main reactor parameters plays a fundamental role not only for criticality safety but also in burnup analyses. In facts, the accurate prediction of nuclear parameters in burnup calculations strongly affects the management of spent nuclear fuel, the core design, as well as the economy and safety of nuclear reactors. In this paper a study related to the impact of the nuclear data uncertainties on the evolution in time of the criticality and the nuclide concentrations in burnup calculations is presented. The analysis has been performed by using a statistical sampling methodology in which all the uncertain parameters are handled as random dependent variables by a sampling procedure. The probability distributions of the uncertain input parameters are used to generate random variations of these input quantities starting from a covariance library in a 56-group energy structure. Calculations have been performed by means of the SCALE 6.2.2 code and ENDF/B-VII.1 nuclear data. The method has been tested on a PWR pin cell model representative of the TMI-1 15 × 15 assembly as defined in an international benchmark exercise. In the first part of the paper the methodology and the neutronics modelling of the problem are presented.
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Naseh Moghanlou, Lida, and Mohammad Pourgol-Mohammad. "Assessment of the Pitting Corrosion Degradation Lifetime: Case Study of Boiler Tubes." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51854.

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In this research, degradation of the metal corrosion for a combined-cycle power plant boiler is investigated. By application of the developed methodology, the corrosion lifetime is estimated for the boiler tubes. At first, with special focus on the corrosion failures, the important failure modes and mechanisms are evaluated for the metallic boiler tubes via FMEA method. It was found that pitting corrosion is one of the most common modes of corrosion. So far, most studies have estimated the lifetime of pitting corrosion using the deterministic data, in which the results are valid only for certain limited conditions. Also, majority of the previous works on the corrosion were conducted experimentally and lesser efforts have been made in the field of the corrosion modeling. The proposed models are based on deterministic approach, however corrosion has a stochastic nature, and it is affected by many stochastic factors. In order to improve deficiencies of available deterministic models, in the present paper, stochastic and uncertainty methods are employed to study the corrosion. The temporal process of metal degradation is analyzed in different conditions through the stochastic approach. A proper degradation model is selected for providing the best estimation of pitting corrosion life. Uncertainty intervals/distributions are determined for some of the model parameters and the parameter intervals are specified. The deterministic model is converted to a probabilistic model by taking to account the variability of the uncertainty input parameters. The model is simulated using Monte Carlo method via simple sampling from the uncertainty distribution of the sources. In order to have better estimation of the parameters and also considering the experimental data in the modeling, the result of the life estimation is updated by the Bayesian method. Finally for the element that is subjected to the pitting corrosion degradation, the distribution of the life estimation is obtained. The uncertainty associated with the result is estimated as the probability of occurrence of each event. Modelling results shows that pitting corrosion has stochastic behavior and the lognormal distribution is the most appropriate option for the pitting corrosion modeling. In order to validate the results, the estimations were compared with the power plant field failure data.
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Vasilj, Andrej, Sebastian Schuster, and Alexander White. "The Influence of Wake Chopping on Wet-Steam Turbine Modelling." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15766.

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Abstract The formation of water droplets within condensing steam turbines is a complex process that occurs at supersaturated, non-equilibrium conditions and is influenced by the unsteady segmentation of blade wakes by successive blade rows. This is often referred to as ‘wake chopping’, and its effect on the condensation process is the subject of this paper. The practical significance is that thermodynamic ‘wetness losses’ (which constitute a major fraction of the overall loss) are strongly affected by droplet size. Likewise, droplet deposition and the various ensuing two-phase phenomena (such as film migration and coarse-water formation) also depend on the spectrum of droplet sizes in the primary fog. The majority of wake-chopping models presented in the literature adopt a stochastic approach, whereby large numbers of fluid particles are tracked through (some representation of) the turbine flowfield, assigning a random number at each successive blade row to represent the particle’s pitchwise location, and hence its level of dissipation. This study contributes to the existing literature by adding: (a) a comprehensive study of the sensitivity to key model parameters (e.g., blade wake shape and wake decay rate); (b) an assessment of the impact of circumferential pressure variations; (c) a study of the implications for wetness losses and (d) a study of the implications for deposition rates.
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