Download or read book Modelling and Parameter Estimation of Dynamic Systems written by J.R. Raol and published by IET. This book was released on 2004-08-13 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation.
Download or read book Parameter Formulation and Estimation in System Dynamic Models written by Alan K. Graham and published by . This book was released on 1976 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Models in Biology written by Stephen P. Ellner and published by Princeton University Press. This book was released on 2011-09-19 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology. Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians. Linked to a Web site with computer-lab materials and exercises, Dynamic Models in Biology is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.
Download or read book Analytical Methods for Dynamic Modelers written by Hazhir Rahmandad and published by MIT Press. This book was released on 2015-11-27 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: A user-friendly introduction to some of the most useful analytical tools for model building, estimation, and analysis, presenting key methods and examples. Simulation modeling is increasingly integrated into research and policy analysis of complex sociotechnical systems in a variety of domains. Model-based analysis and policy design inform a range of applications in fields from economics to engineering to health care. This book offers a hands-on introduction to key analytical methods for dynamic modeling. Bringing together tools and methodologies from fields as diverse as computational statistics, econometrics, and operations research in a single text, the book can be used for graduate-level courses and as a reference for dynamic modelers who want to expand their methodological toolbox. The focus is on quantitative techniques for use by dynamic modelers during model construction and analysis, and the material presented is accessible to readers with a background in college-level calculus and statistics. Each chapter describes a key method, presenting an introduction that emphasizes the basic intuition behind each method, tutorial style examples, references to key literature, and exercises. The chapter authors are all experts in the tools and methods they present. The book covers estimation of model parameters using quantitative data; understanding the links between model structure and its behavior; and decision support and optimization. An online appendix offers computer code for applications, models, and solutions to exercises. Contributors Wenyi An, Edward G. Anderson Jr., Yaman Barlas, Nishesh Chalise, Robert Eberlein, Hamed Ghoddusi, Winfried Grassmann, Peter S. Hovmand, Mohammad S. Jalali, Nitin Joglekar, David Keith, Juxin Liu, Erling Moxnes, Rogelio Oliva, Nathaniel D. Osgood, Hazhir Rahmandad, Raymond Spiteri, John Sterman, Jeroen Struben, Burcu Tan, Karen Yee, Gönenç Yücel
Download or read book Parameters Estimation Using Bootstrapping For System Dynamics Models written by Mohammed Mesabbah and published by LAP Lambert Academic Publishing. This book was released on 2014-05 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: In system dynamics models, distributions for uncertain parameters are required for testing model sensitivity. As well in the same context, it could be used to test sensitivity and robustness for new policies. Moreover, these distributions could be used for confidence intervals estimation and hypothesis testing. Residual bootstrapping technique (RBT) is one of bootstrapping techniques that could be used for estimating parameters distributions for system dynamics models. RBT is a statistical technique based on resampling residuals for fabricating "new historical data." These new historical data is used to fit the model and get new estimates for the parameters. Repeating this many times will generate many estimates for the parameter(s). In this work a proposed method based on RBT is used to improve the bias that could be resulted from estimation. The commodity cycle model for hogs in USA is used as case study. Some table relationships in this model have been replaced by nonlinear functions. RBT is used for estimating parameters distributions for these nonlinear functions.
Download or read book Dynamic Systems Models written by Josif A. Boguslavskiy and published by Springer. This book was released on 2016-03-22 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is an exposition of a novel method for solving inverse problems, a method of parameter estimation for time series data collected from simulations of real experiments. These time series might be generated by measuring the dynamics of aircraft in flight, by the function of a hidden Markov model used in bioinformatics or speech recognition or when analyzing the dynamics of asset pricing provided by the nonlinear models of financial mathematics. Dynamic Systems Models demonstrates the use of algorithms based on polynomial approximation which have weaker requirements than already-popular iterative methods. Specifically, they do not require a first approximation of a root vector and they allow non-differentiable elements in the vector functions being approximated. The text covers all the points necessary for the understanding and use of polynomial approximation from the mathematical fundamentals, through algorithm development to the application of the method in, for instance, aeroplane flight dynamics or biological sequence analysis. The technical material is illustrated by the use of worked examples and methods for training the algorithms are included. Dynamic Systems Models provides researchers in aerospatial engineering, bioinformatics and financial mathematics (as well as computer scientists interested in any of these fields) with a reliable and effective numerical method for nonlinear estimation and solving boundary problems when carrying out control design. It will also be of interest to academic researchers studying inverse problems and their solution.
Download or read book Parameter Estimation of Structural Dynamic Models Using Eigenvalue and Eigenvector Information written by and published by . This book was released on 1990 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structural system identification methods are analytical techniques for reconciling test data with analytical models. The response data frequently used to compare a finite element model and test data are the eigenvalues of the system. However, eigenvalues alone cannot assure an adequate model. Eigenvectors also provide valuable information for the process of updating finite element models. For large order, complex finite element models, ad-hoc procedures have often proven inadequate for model parameter updating. Therefore, parameter estimation techniques such as Bayes estimation or mathematical programming have been applied. Mathematical programming techniques can be use for parameter estimation allowing a very general definition of the objective function and constraints. This paper will present the application of mathematical programming techniques of parameter estimation to the updating of a finite element model of an electronic package. The following topics will be discussed in the paper. The mathematical programming formulation of the parameter estimation problem, which uses both eigenvalue and eigenvector response data. The software implementation of this technique. The application of this methodology to the estimation of parameters of an electronics package model.
Download or read book System Dynamics written by Bilash Kanti Bala and published by Springer. This book was released on 2016-10-28 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the broad spectrum of system dynamics methodologies for the modelling and simulation of complex systems: systems thinking, causal diagrams, systems structure of stock and flow diagrams, parameter estimation and tests for confidence building in system dynamics models. It includes a comprehensive review of model validation and policy design and provides a practical presentation of system dynamics modelling. It also offers numerous worked-out examples and case studies in diverse fields using STELLA and VENSIM. The system dynamics methodologies presented here can be applied to nearly all areas of research and planning, and the simulations provided make the complicated issues more easily understandable. System Dynamics: Modelling and Simulation is an essential system dynamics and systems engineering textbook for undergraduate and graduate courses. It also offers an excellent reference guide for managers in industry and policy planners who wish to use modelling and simulation to manage complex systems more effectively, as well as researchers in the fields of modelling and simulation-based systems thinking.
Download or read book System Dynamics Modeling with R written by Jim Duggan and published by Springer. This book was released on 2016-06-14 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new interdisciplinary work presents system dynamics as a powerful approach to enable analysts build simulation models of social systems, with a view toward enhancing decision making. Grounded in the feedback perspective of complex systems, the book provides a practical introduction to system dynamics, and covers key concepts such as stocks, flows, and feedback. Societal challenges such as predicting the impact of an emerging infectious disease, estimating population growth, and assessing the capacity of health services to cope with demographic change can all benefit from the application of computer simulation. This text explains important building blocks of the system dynamics approach, including material delays, stock management heuristics, and how to model effects between different systemic elements. Models from epidemiology, health systems, and economics are presented to illuminate important ideas, and the R programming language is used to provide an open-source and interoperable way to build system dynamics models. System Dynamics Modeling with R also describes hands-on techniques that can enhance client confidence in system dynamic models, including model testing, model analysis, and calibration. Developed from the author’s course in system dynamics, this book is written for undergraduate and postgraduate students of management, operations research, computer science, and applied mathematics. Its focus is on the fundamental building blocks of system dynamics models, and its choice of R as a modeling language make it an ideal reference text for those wishing to integrate system dynamics modeling with related data analytic methods and techniques.
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.
Download or read book Identification of Dynamic Systems written by Rolf Isermann and published by Springer. This book was released on 2011-04-08 with total page 705 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the measurement. The book is theory-oriented and application-oriented and most methods covered have been used successfully in practical applications for many different processes. Illustrative examples in this book with real measured data range from hydraulic and electric actuators up to combustion engines. Real experimental data is also provided on the Springer webpage, allowing readers to gather their first experience with the methods presented in this book. Among others, the book covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation with a focus on the method of Least Squares and modifications, identification of time-variant processes, identification in closed-loop, identification of continuous time processes, and subspace methods. Some methods for nonlinear system identification are also considered, such as the Extended Kalman filter and neural networks. The different methods are compared by using a real three-mass oscillator process, a model of a drive train. For many identification methods, hints for the practical implementation and application are provided. The book is intended to meet the needs of students and practicing engineers working in research and development, design and manufacturing.
Download or read book System Dynamics written by Augusto Legasto and published by North Holland. This book was released on 1980 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: System dynamics: future opportunities and a critical review; Modeling issues and decisions in system dynamics; Methods for enhancing refutability in system dynamics modeling; Time in system dynamics; Toward a pedagogy of system dynamics; The multiplier-accelerator model of business cycles interpreted from a system dynamics perspective; Parameter estimation in system dynamics modeling; Some effects of data error on econometric models; COLTS (continous long-term simulation); Integration method: euler or other for system dynamics; Including future events in system dynamics models; Tests for building confidence in system dynamics models; Modal analysis to aid system dynamics simulation; Which policy run is best, and who says so?
Download or read book Measurement Data Modeling and Parameter Estimation written by Zhengming Wang and published by CRC Press. This book was released on 2016-04-19 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the theories, methods, and application techniques of the measurement data mathematical modeling and parameter estimation. It seeks to build a bridge between mathematical theory and engineering practice in the measurement data processing field so theoretical researchers and technical engineers can communicate. It is organized with abundant materials, such as illustrations, tables, examples, and exercises. The authors create examples to apply mathematical theory innovatively to measurement and control engineering. Not only does this reference provide theoretical knowledge, it provides information on first hand experiences.
Download or read book Dynamic Process Modeling written by and published by John Wiley & Sons. This book was released on 2013-10-02 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inspired by the leading authority in the field, the Centre for Process Systems Engineering at Imperial College London, this book includes theoretical developments, algorithms, methodologies and tools in process systems engineering and applications from the chemical, energy, molecular, biomedical and other areas. It spans a whole range of length scales seen in manufacturing industries, from molecular and nanoscale phenomena to enterprise-wide optimization and control. As such, this will appeal to a broad readership, since the topic applies not only to all technical processes but also due to the interdisciplinary expertise required to solve the challenge. The ultimate reference work for years to come.
Download or read book Small Sample Parameter Estimation for Forced Discrete Linear Dynamic Models written by Donald L. Stevens and published by . This book was released on 1979 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of estimating the parameters of a forced discrete linear dynamic model is considered. The system model is conceptualized to include the value of the initial state as a parameter. The forces driving the system are partitioned into accessible and inaccessible inputs. Accessible inputs are those that are measured; inaccessible inputs are all others, including random disturbances. Maximum likelihood and mean upper likelihood estimators are derived. The mean upper likelihood estimator is a variant of the mean likelihood estimator and apparently has more favorable small sample properties than does the maximum likelihood estimator. A computational algorithm that does not require the inversion or storage of large matrices is developed. The estimators and the algorithm are derived for models having an arbitrary number of inputs and a single output. The extension to a two output system is illustrated; further extension to an arbitrary number of outputs follows trivially. The techniques were developed for the analysis of possibly unique realizations of a process. The assumption that the inaccessible input is a stationary process is necessary only over the period of observation. Freedom from the more general usual assumptions was made possible by treatment of the initial state as a parameter. The derived estimation technique should be particularly suitable for the analysis of observational data. Simulation studies are used to compare the estimators and assess their properties. The mean upper likelihood estimator has consistently smaller mean square error than does the maximum likelihood estimator. An example application is presented, representing a unique realization of a dynamic system. The problems associated with determination of concurrence of a hypothetical "system change" with a temporally identified event are examined, and associated problems of inference of causality based on observational data are discussed with respect to the example.
Download or read book Modeling Identification and Simulation of Dynamical Systems written by P. P. J. van den Bosch and published by CRC Press. This book was released on 2020-12-17 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives an in-depth introduction to the areas of modeling, identification, simulation, and optimization. These scientific topics play an increasingly dominant part in many engineering areas such as electrotechnology, mechanical engineering, aerospace, and physics. This book represents a unique and concise treatment of the mutual interactions among these topics. Techniques for solving general nonlinear optimization problems as they arise in identification and many synthesis and design methods are detailed. The main points in deriving mathematical models via prior knowledge concerning the physics describing a system are emphasized. Several chapters discuss the identification of black-box models. Simulation is introduced as a numerical tool for calculating time responses of almost any mathematical model. The last chapter covers optimization, a generally applicable tool for formulating and solving many engineering problems.
Download or read book Dynamic Model Development Methods Theory and Applications written by S. Macchietto and published by Elsevier. This book was released on 2003-08-04 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Detailed mathematical models are increasingly being used by companies to gain competitive advantage through such applications as model-based process design, control and optimization. Thus, building various types of high quality models for processing systems has become a key activity in Process Engineering. This activity involves the use of several methods and techniques including model solution techniques, nonlinear systems identification, model verification and validation, and optimal design of experiments just to name a few. In turn, several issues and open-ended problems arise within these methods, including, for instance, use of higher-order information in establishing parameter estimates, establishing metrics for model credibility, and extending experiment design to the dynamic situation. The material covered in this book is aimed at allowing easier development and full use of detailed and high fidelity models. Potential applications of these techniques in all engineering disciplines are abundant, including applications in chemical kinetics and reaction mechanism elucidation, polymer reaction engineering, and physical properties estimation. On the academic side, the book will serve to generate research ideas. Contains wide coverage of statistical methods applied to process modelling Serves as a recent compilation of dynamic model building tools Presents several examples of applying advanced statistical and modelling methods to real process systems problems
