Last edited by Kagall
Saturday, May 9, 2020 | History

5 edition of Nonlinear estimation found in the catalog.

Nonlinear estimation

by Gavin J. S. Ross

  • 232 Want to read
  • 21 Currently reading

Published by Springer-Verlag in New York .
Written in English

    Subjects:
  • Estimation theory,
  • Nonlinear theories

  • Edition Notes

    Includes bibliographical references (p. [178]-182) and index.

    StatementGavin J.S. Ross.
    SeriesSpringer series in statistics
    Classifications
    LC ClassificationsQA276.8 .R67 1990
    The Physical Object
    Paginationviii, 189 p. :
    Number of Pages189
    ID Numbers
    Open LibraryOL1873099M
    ISBN 100387972781, 3540972781
    LC Control Number90032797

    The book describes how sparse optimization methods can be combined with discretization techniques for differential-algebraic equations and used to solve optimal control and estimation problems. The interaction between optimization and integration is emphasized throughout the book. The second aim of this paper is to combine H ∞ control, robustness analysis and set-membership estimation theories for the adaptive control of a nonlinear plant. As the robust estimator provides a measure of the uncertainties in the estimated model, one can bypass the certainty equivalence principle and adjust on line the achievable.

    Compare book prices from over , booksellers. Find Studies in nonlinear estimation () by Stephen M. Goldfeld;. The Mathematical Sciences Research Institute (MSRI), founded in , is an independent nonprofit mathematical research institution whose funding sources include the National Science Foundation, foundations, corporations, and more than 90 universities and institutions. The Institute is located at 17 Gauss Way, on the University of California, Berkeley campus, close to .

    This book is a revision of Nonlinear Filters: Estimation and Applications, (Lecture Notes in Economics and Mathematical Systems, No), which was published from Springer-Verlag in Compared with the first edition, I have made a substantial revision in the second edition. First, titles in the following chapters, sections, terms and so on areFile Size: KB. Nonlinear algebraic equations, which are also called polynomial equations, are defined by equating polynomials (of degree greater than one) to zero. For example, + −. For a single polynomial equation, root-finding algorithms can be used to find solutions to the equation (i.e., sets of values for the variables that satisfy the equation). However, systems of algebraic .


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Nonlinear estimation by Gavin J. S. Ross Download PDF EPUB FB2

The book starts from the basic conceptual solution of a nonlinear estimation problem and provides an in depth coverage of (i) various Gaussian filters such as the unscented Kalman filter, cubature and quadrature based filters, Gauss-Hermite filter and their variants and (ii) Gaussian sum filter, in both discrete and continuous-discrete domain.

Non-Linear Estimation is a handbook for the practical statistician or modeller interested in fitting and interpreting non-linear models with the aid of a computer.

A major theme of the book is the use of 'stable parameter systems'; these provide rapid convergence of optimization algorithms, more reliable dispersion matrices and confidence regions for parameters, and easier Format: Paperback.

Nonlinear Estimation and Control of Automotive Drivetrains discusses the control problems involved in automotive drivetrains, particularly in hydraulic Automatic Transmission (AT), Dual Clutch Transmission (DCT) and Automated Manual Transmission (AMT).

Challenging estimation and control problems, such as driveline torque estimation and gear shift control, are addressed Cited by: 9. Non-Linear Estimation is a handbook for the practical statistician or modeller interested in fitting and interpreting non-linear models with the aid of a computer.

A major theme of the book is the use of 'stable parameter systems'; these provide rapid convergence of optimization algorithms, more reliable dispersion matrices and confidence regions for parameters, and easier. Book Description. Grid-based Nonlinear Estimation and its Applications presents new Bayesian nonlinear estimation techniques developed in the last two decades.

Grid-based estimation techniques are based on efficient and precise numerical integration rules to improve performance of the traditional Kalman filtering based estimation for nonlinear and uncertainty dynamic.

The book can also serve as a core graduate text for a course on state estimation. The book starts from the basic conceptual solution Nonlinear estimation book a nonlinear estimation problem and provides an in depth coverage of (i) various Gaussian filters such as the unscented Kalman filter, cubature and quadrature based filters, Gauss-Hermite filter and their.

Non-Linear Estimation is a handbook for the practical statistician or modeller interested in fitting and interpreting non-linear models with the aid of a computer.

A major theme of the book is the use of 'stable parameter systems'; these provide rapid convergence of optimization algorithms, more reliable dispersion matrices and confidence regions for parameters, and easier Brand: Springer-Verlag New York.

This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of.

Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking Book Abstract: Bayesian Bounds provides a collection of the important papers dealing with the theory and application of Bayesian bounds.

The book is essential to both engineers and statisticians whether they are practitioners or theorists. Each part of the book is. The density-based nonlinear filters introduced in this book utilize numerical integration, Monte-Carlo integration with importance sampling or rejection sampling and the obtained filtering estimates are asymptotically unbiased and efficient.

By Monte-Carlo simulation studies, all the nonlinear filters are : Springer-Verlag Berlin Heidelberg. A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system.

The author presents Brand: Wiley. This book focuses on several key aspects of nonlinear systems including dynamic modeling, state estimation, and stability analysis. It is intended to provide a wide range of readers in applied mathematics and various engineering disciplines an excellent survey of recent studies of nonlinear systems.

With its thirteen chapters, the book brings together important contributions Author: Mahmut Reyhanoglu. Books shelved as non-linear: An Ishmael of Syria by Asaad Almohammad, Slaughterhouse-Five by Kurt Vonnegut Jr., A Visit from the Goon Squad by Jennifer E.

Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, processes.

this book. I believe that from these themes will be forged many useful engineering tools for dealing with nonlinear systems in the future. But a note of caution is appropriate. Nonlinear systems do not yield easily to analysis, especially in the sense that for a given analytical method it is not hard to find an inscrutable system.

Nonlinear estimation. New York: Springer-Verlag, © (OCoLC) Material Type: Internet resource: Document Type: Book, Internet Resource: All Authors / Contributors: Gavin J S Ross. Find more information about: ISBN: # Nonlinear theories\/span>\n \u00A0\u00A0\u00A0\n schema.

About this book Introduction Using an appealing Pythagorean-like geometry of the empirical and model distributions, the book brings a new solution to the problem of recursive estimation of non-Gaussian and nonlinear models which can be regarded as a.

Introduction to Nonlinear Regression Andreas Ruckstuhl IDP Institut für Datenanalyse und Prozessdesign ZHAW Zürcher Hochschule für Angewandte Wissenschaften October ∗† Contents 1.

The Nonlinear Regression Model 1 2. Methodology for Parameter Estimation 5 3. Approximate Tests and Confidence Intervals 8 Size: KB. COVID Resources. Reliable information about the coronavirus (COVID) is available from the World Health Organization (current situation, international travel).Numerous and frequently-updated resource results are available from this ’s WebJunction has pulled together information and resources to assist library staff as they consider how to handle.

The nonlinear estimation is applied for a calibration graph with an exponential shape, where the previously described algorithms can be employed again. Select 8 - The titration system Book chapter Full text access. Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters (m ≥ n).It is used in some forms of nonlinear basis of the method is to approximate the model by a linear one and to refine the parameters by successive iterations.Iterative Methods for Linear and Nonlinear Equations C.

T. Kelley North Carolina State University Society for Industrial and Applied Mathematics Though this book is written in a finite-dimensional setting, we have selected for coverage mostlyalgorithms and methods of File Size: KB.

The unscented Kalman filter for nonlinear estimation Abstract: This paper points out the flaws in using the extended Kalman filter (EKE) and introduces an improvement, the unscented Kalman filter (UKF), proposed by Julier and Uhlman ().

A central and vital operation performed in the Kalman filter is the propagation of a Gaussian random Cited by: