Measurement Data Modeling and Parameter Estimation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Measurement Data Modeling and Parameter Estimation write by Zhengming Wang. This book was released on 2016-04-19. Measurement Data Modeling and Parameter Estimation available in PDF, EPUB and Kindle. 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.
Modelling and Parameter Estimation of Dynamic Systems
Modelling and Parameter Estimation of Dynamic Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Modelling and Parameter Estimation of Dynamic Systems write by J.R. Raol. This book was released on 2004-08-13. Modelling and Parameter Estimation of Dynamic Systems available in PDF, EPUB and Kindle. 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.
Model Based Parameter Estimation
Model Based Parameter Estimation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Model Based Parameter Estimation write by Hans Georg Bock. This book was released on 2013-02-26. Model Based Parameter Estimation available in PDF, EPUB and Kindle. 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.
Identification of Continuous-Time Systems
Identification of Continuous-Time Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Identification of Continuous-Time Systems write by Allamaraju Subrahmanyam. This book was released on 2019-12-06. Identification of Continuous-Time Systems available in PDF, EPUB and Kindle. Models of dynamical systems are required for various purposes in the field of systems and control. The models are handled either in discrete time (DT) or in continuous time (CT). Physical systems give rise to models only in CT because they are based on physical laws which are invariably in CT. In system identification, indirect methods provide DT models which are then converted into CT. Methods of directly identifying CT models are preferred to the indirect methods for various reasons. The direct methods involve a primary stage of signal processing, followed by a secondary stage of parameter estimation. In the primary stage, the measured signals are processed by a general linear dynamic operation—computational or realized through prefilters, to preserve the system parameters in their native CT form—and the literature is rich on this aspect. In this book: Identification of Continuous-Time Systems-Linear and Robust Parameter Estimation, Allamaraju Subrahmanyam and Ganti Prasada Rao consider CT system models that are linear in their unknown parameters and propose robust methods of estimation. This book complements the existing literature on the identification of CT systems by enhancing the secondary stage through linear and robust estimation. In this book, the authors provide an overview of CT system identification, consider Markov-parameter models and time-moment models as simple linear-in-parameters models for CT system identification, bring them into mainstream model parameterization via basis functions, present a methodology to robustify the recursive least squares algorithm for parameter estimation of linear regression models, suggest a simple off-line error quantification scheme to show that it is possible to quantify error even in the absence of informative priors, and indicate some directions for further research. This modest volume is intended to be a useful addition to the literature on identifying CT systems.
Model Calibration and Parameter Estimation
Model Calibration and Parameter Estimation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Model Calibration and Parameter Estimation write by Ne-Zheng Sun. This book was released on 2015-07-01. Model Calibration and Parameter Estimation available in PDF, EPUB and Kindle. 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.
