Download or read book Model Parametric Sensitivity Analysis and Nonlinear Parameter Estimation written by Malamas Caracotsios and published by . This book was released on 1986 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Online Optimization of Large Scale Systems written by Martin Grötschel and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 789 pages. Available in PDF, EPUB and Kindle. Book excerpt: In its thousands of years of history, mathematics has made an extraordinary ca reer. It started from rules for bookkeeping and computation of areas to become the language of science. Its potential for decision support was fully recognized in the twentieth century only, vitally aided by the evolution of computing and communi cation technology. Mathematical optimization, in particular, has developed into a powerful machinery to help planners. Whether costs are to be reduced, profits to be maximized, or scarce resources to be used wisely, optimization methods are available to guide decision making. Opti mization is particularly strong if precise models of real phenomena and data of high quality are at hand - often yielding reliable automated control and decision proce dures. But what, if the models are soft and not all data are around? Can mathematics help as well? This book addresses such issues, e. g. , problems of the following type: - An elevator cannot know all transportation requests in advance. In which order should it serve the passengers? - Wing profiles of aircrafts influence the fuel consumption. Is it possible to con tinuously adapt the shape of a wing during the flight under rapidly changing conditions? - Robots are designed to accomplish specific tasks as efficiently as possible. But what if a robot navigates in an unknown environment? - Energy demand changes quickly and is not easily predictable over time. Some types of power plants can only react slowly.

Download or read book Global Sensitivity Analysis written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2008-02-28 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard. Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative sensitivity analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications. The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials. Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications. Is authored by leading sensitivity analysis practitioners, combining a range of disciplinary backgrounds. Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk analysis and to financial analysts concerned with pricing and hedging.

Download or read book Nonlinear Sensitivity Analysis of Multi Parameter Model Systems written by R. I. Cukier and published by . This book was released on 1974 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large sets of coupled, nonlinear equations arise in a number of disciplines in connection with computer based models of physical, social and economic processes. Solutions for such large systems of equations must be effected by means of digital computers using appropriately designed codes. This paper addresses itself to the critically important problem of how sensitive the solutions are to variations of, or inherent uncertainties in, the parameters of the equation set. We review here, and also present further developments, of our statistical method of sensitivity analysis. The sensitivity analysis presented here is nonlinear and thus permits one to study the effects of large deviations from the nominal parameter values. In addition, since all parameters are varied simultaneously, one can explore regions of parameter space where several parameters deviate simultaneously from their nominal values. Developed her eis a theory of a method of sensitivity analysis, then detail the method of implementation and finally present several examples of its use to date.

Download or read book Model Calibration and Parameter Estimation written by Ne-Zheng Sun and published by Springer. This book was released on 2015-07-01 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-part book provides a comprehensive and systematic introduction to these challenging topics such as model calibration, parameter estimation, reliability assessment, and data collection design. Part 1 covers the classical inverse problem for parameter estimation in both deterministic and statistical frameworks, Part 2 is dedicated to system identification, hyperparameter estimation, and model dimension reduction, and Part 3 considers how to collect data and construct reliable models for prediction and decision-making. For the first time, topics such as multiscale inversion, stochastic field parameterization, level set method, machine learning, global sensitivity analysis, data assimilation, model uncertainty quantification, robust design, and goal-oriented modeling, are systematically described and summarized in a single book from the perspective of model inversion, and elucidated with numerical examples from environmental and water resources modeling. Readers of this book will not only learn basic concepts and methods for simple parameter estimation, but also get familiar with advanced methods for modeling complex systems. Algorithms for mathematical tools used in this book, such as numerical optimization, automatic differentiation, adaptive parameterization, hierarchical Bayesian, metamodeling, Markov chain Monte Carlo, are covered in details. This book can be used as a reference for graduate and upper level undergraduate students majoring in environmental engineering, hydrology, and geosciences. It also serves as an essential reference book for professionals such as petroleum engineers, mining engineers, chemists, mechanical engineers, biologists, biology and medical engineering, applied mathematicians, and others who perform mathematical modeling.

Download or read book Direct decoupled sensitivity analysis of differential systems with nonlinear application to parameter estimation written by Patrick S. McCroskey and published by . This book was released on 1985 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Sensitivity Analysis in Practice written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2004-07-16 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB – a widely distributed freely-available sensitivity analysis software package developed by the authors – for solving problems in sensitivity analysis of statistical models. Other key features: Provides an accessible overview of the current most widely used methods for sensitivity analysis. Opens with a detailed worked example to explain the motivation behind the book. Includes a range of examples to help illustrate the concepts discussed. Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors. Contains a large number of references to sources for further reading. Authored by the leading authorities on sensitivity analysis.

Download or read book The Prevention and Treatment of Missing Data in Clinical Trials written by National Research Council and published by National Academies Press. This book was released on 2010-12-21 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.

Download or read book Advances in Sensitivity Analysis and Parametric Programming written by Tomas Gal and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: The standard view of Operations Research/Management Science (OR/MS) dichotomizes the field into deterministic and probabilistic (nondeterministic, stochastic) subfields. This division can be seen by reading the contents page of just about any OR/MS textbook. The mathematical models that help to define OR/MS are usually presented in terms of one subfield or the other. This separation comes about somewhat artificially: academic courses are conveniently subdivided with respect to prerequisites; an initial overview of OR/MS can be presented without requiring knowledge of probability and statistics; text books are conveniently divided into two related semester courses, with deterministic models coming first; academics tend to specialize in one subfield or the other; and practitioners also tend to be expert in a single subfield. But, no matter who is involved in an OR/MS modeling situation (deterministic or probabilistic - academic or practitioner), it is clear that a proper and correct treatment of any problem situation is accomplished only when the analysis cuts across this dichotomy.

Download or read book Online Parameter Identification for Optimal Feedback Control of Nonlinear Dynamical Systems written by Margareta Runge and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research aims to enhance current methods for the optimal feedback control of complex nonlinear dynamical systems via online parameter identifications. Accurate knowledge of the system parameters is essential in numerous practical applications to ensure effective control. A considerable number of advanced control algorithms use model-based approaches. However, the model parameters may often be unknown or subject to change over time. This could result in deviations from the feedback control objective, increased expected costs, and even divergence of the controller. The main objective of this thesis is to develop a combined online parameter identification and model-based controller approach that allows continuously estimating the model parameters of a nonlinear system. The available real-time measurements of the system are used to compute an approximation of the searched parameters. This repeated parameter estimation enables the control algorithm to adapt to the changing system dynamics and maintain optimal control accuracy. This study investigates three approaches. First, a coupled algorithm is developed that employs parameter identifications during operation to adapt a linear quadratic regulator using techniques from parametric sensitivity analysis. Additionally, an approach is presented that also examines the information quality in the data used to predict the probability of success of the parameter estimation. An adaptive control algorithm using nonlinear model predictive control (NMPC) and online parameter identification is proposed as a third alternative. All proposed techniques rely on highly efficient numerical methods for solving nonlinear optimization problems (NLP) and the potential to transfer related problems from optimal control into an NLP by discretization. The proposed approaches are extensively evaluated by conducting simulations and comparing them to the existing standard control methods.

Download or read book Parametric Sensitivity Analysis of Stochastic Reaction Networks written by Ting Wang and published by . This book was released on 2015 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reaction networks are systems consisting of several species interacting with each other through a set of predefined reaction channels.Models of real world reaction systems often contain several parameters which play a significant role in determining the system's dynamics. Therefore, parametric sensitivity analysis is an essential tool for the modeling and parameter estimation process. Due to the complex and random nature of the reaction systems, among all approaches for sensitivity analysis, Monte Carlo simulation is the most suitable for the parametric sensitivity analysis because its complexity does not grow dramatically as the problem dimension grows. Most Monte Carlo methods for sensitivity analysis can be classified into three categories, the pathwise derivative (PD), the finite difference (FD) and the Girsanov transformation (GT). Comparisons of these methods for specific examples have been done by many researchers, which showed that when applicable, the PD method and FD method tend to outperform the GT method. However, to the best of our knowledge, no existing literature studies these observations from a theoretical point of view. In this thesis, we provide a theoretical justification for these observations in terms of system size asymptotic analysis. We also examine our result by testing several numerical examples. Other than the analysis for the efficiency of these Monte Carlo estimators, we also provide some sufficient conditions which guarantee the validity of the GT method. Finally, for an ergodic system, there exists a steady state distribution and hence it is reasonable for us to consider the steady state sensitivity estimation problem. We establish an asymptotic correlation result and use this result to justify the ensemble-averaged correlation function method introduced in the literature.

Download or read book Sensitivity Analysis for Parametric Non Linear Programming Using Penalty Methods written by Robert L. Armacost and published by . This book was released on 1976 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, it has been shown that a class of penalty function algorithms can readily be adapted to generate sensitivity analysis information for a large class of parametric nonlinear programming problems. In particular, estimates of the partial derivatives (with respect to the problem parameters) of the components of a solution vector and the optimal value function have been successfully calculated for a number of nontrivial examples. The approach has been implemented using the well known Sequential Unconstrained Minimization Technique (SUMT) computer program. This paper, a continuation and amplification of a recent paper by Armacost, gives a detailed summary of the significant underlying theoretical results, reviews recent additions to the computer program that include Lagrange multiplier sensitivity calculations, and elaborates on the kind of information that can be generated by further analyzing and interpreting results obtained in applying the techique to a well known inventory model. (Author).

Download or read book Second Order Parametric Sensitivity Analysis in NLP and Estimates by Penalty Function Methods written by Robert L. Armacost and published by . This book was released on 1975 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pursuing a number of theoretical results recently obtained by Fiacco, this paper continues the development of a basis for calculating first-order changes in a Kuhn-Tucker triple and second-order changes in the optimal value function of a class of general parametric nonlinear programming problems, with respect to a perturbation of the problem parameters. Exploiting problem structure, specific formulas are derived for calculating the first partial derivatives of a Kuhn-Tucker triple. Approximations to these quantities are obtained in parallel throughout, by way of an associated logarithmic-quadratic penalty function. Applications are indicated.

Download or read book Computer Aided Modeling of Reactive Systems written by Warren E. Stewart and published by John Wiley & Sons. This book was released on 2008-03-17 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to apply modeling and parameter estimation tools and strategies to chemicalprocesses using your personal computer This book introduces readers to powerful parameter estimation and computational methods for modeling complex chemical reactions and reaction processes. It presents useful mathematical models, numerical methods for solving them, and statistical methods for testing and discriminating candidate models with experimental data. Topics covered include: Chemical reaction models Chemical reactor models Probability and statistics Bayesian estimation Process modeling with single-response data Process modeling with multi-response data Computer software (Athena Visual Studio) is available via a related Web site http://www.athenavisual.com enabling readers to carry out parameter estimation based on their data and to carry out process modeling using these parameters. As an aid to the reader, an appendix of example problems and solutions is provided. Computer-Aided Modeling of Reactive Systems is an ideal supplemental text for advanced undergraduates and graduate students in chemical engineering courses, while it also serves as a valuable resource for practitioners in industry who want to keep up to date on the most current tools and strategies available.

Download or read book Computational Mathematical Programming written by Klaus Schittkowski and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the written versions of main lectures presented at the Advanced Study Institute (ASI) on Computational Mathematical Programming, which was held in Bad Windsheim, Germany F. R., from July 23 to August 2, 1984, under the sponsorship of NATO. The ASI was organized by the Committee on Algorithms (COAL) of the Mathematical Programming Society. Co-directors were Karla Hoffmann (National Bureau of Standards, Washington, U.S.A.) and Jan Teigen (Rabobank Nederland, Zeist, The Netherlands). Ninety participants coming from about 20 different countries attended the ASI and contributed their efforts to achieve a highly interesting and stimulating meeting. Since 1947 when the first linear programming technique was developed, the importance of optimization models and their mathematical solution methods has steadily increased, and now plays a leading role in applied research areas. The basic idea of optimization theory is to minimize (or maximize) a function of several variables subject to certain restrictions. This general mathematical concept covers a broad class of possible practical applications arising in mechanical, electrical, or chemical engineering, physics, economics, medicine, biology, etc. There are both industrial applications (e.g. design of mechanical structures, production plans) and applications in the natural, engineering, and social sciences (e.g. chemical equilibrium problems, christollography problems).

Download or read book Parametric Sensitivity in Chemical Systems written by Arvind Varma and published by Cambridge University Press. This book was released on 1999-03-13 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: The behavior of a chemical system is affected by many physicochemical parameters. The sensitivity of the system's behavior to changes in parameters is known as parametric sensitivity. When a system operates in a parametrically sensitive region, its performance becomes unreliable and changes sharply with small variations in parameters. Thus, it would be of great value to predict sensitivity behavior in chemical systems. This book is the first to provide a thorough treatment of the concept of parametric sensitivity and the mathematical tool it generated, sensitivity analysis. The emphasis is on applications to real situations. The book begins with definitions of various sensitivity indices and describes the numerical techniques used for their evaluation. Extensively illustrated chapters discuss sensitivity analysis in a variety of chemical reactors - batch, tubular, continuous-flow, fixed-bed - and in combustion systems, air pollution, and metabolic processes. Chemical engineers, chemists, graduate students, and researchers will welcome this valuable resource.

Download or read book Model Based Parameter Estimation written by Hans Georg Bock and published by Springer Science & Business Media. This book was released on 2013-02-26 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: This judicious selection of articles combines mathematical and numerical methods to apply parameter estimation and optimum experimental design in a range of contexts. These include fields as diverse as biology, medicine, chemistry, environmental physics, image processing and computer vision. The material chosen was presented at a multidisciplinary workshop on parameter estimation held in 2009 in Heidelberg. The contributions show how indispensable efficient methods of applied mathematics and computer-based modeling can be to enhancing the quality of interdisciplinary research. The use of scientific computing to model, simulate, and optimize complex processes has become a standard methodology in many scientific fields, as well as in industry. Demonstrating that the use of state-of-the-art optimization techniques in a number of research areas has much potential for improvement, this book provides advanced numerical methods and the very latest results for the applications under consideration.