Go to main content

Dynamic systems models [electronic resource] : new methods of parameter and state estimation / Josif A. Boguslavskiy ; Mark Borodovsky, editor.

Boguslavskiĭ, I. A. (Iosif Arkadʹevich), author.; Borodovsky, Mark, editor.
2016
QA276.8
Available Online
Formats
Format
BibTeX
MARCXML
TextMARC
MARC
DublinCore
EndNote
NLM
RefWorks
RIS
Add to Basket
Cite
Citation

Linked e-resources

Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks

Details

Title
Dynamic systems models [electronic resource] : new methods of parameter and state estimation / Josif A. Boguslavskiy ; Mark Borodovsky, editor.
Author
Boguslavskiĭ, I. A. (Iosif Arkadʹevich), author.
ISBN
9783319040363 (electronic book)
3319040367 (electronic book)
9783319040356
DOI
https://doi.org/10.1007/978-3-319-04036-3
Published
Cham : Springer, 2016.
Language
English
Description
1 online resource (xx, 201 pages)
Item Number
10.1007/978-3-319-04036-3 doi
Call Number
QA276.8
Dewey Decimal Classification
519.5/44
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 31, 2016).
Added Author
Borodovsky, Mark, editor.
Available in Other Form
Print version: 9783319040356
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
From the Contents: Linear Estimators of a Random-Parameter Vector.-Basis of the Method of Polynomial Approximation
Polynomial Approximation and Optimization of Control
Polynomial Approximation Technique Applied to Inverse Vector Functions
Identification of Parameters of Nonlinear Dynamical Systems: Smoothing, Filtering and Forecasting the State Vector
Estimating Status Vectors from Sight Angles
Estimation of Parameters of Stochastic Models
Designing the Control of Motion to a Target Point of Phase Space
Inverse Problems of Dynamics Algorithm for Identifying Parameters of an Aircraft.

Browse Subjects

Show more subjects...

Statistics

from
to
Export