國立虎尾科技大學 |

Dynamic Systems Models = New Methods of Parameter and State Estimation /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Dynamic Systems Models/ by Josif A. Boguslavskiy ; edited by Mark Borodovsky.
Reminder of title:	New Methods of Parameter and State Estimation /
Author:	Boguslavskiy, Josif A.
other author:	Borodovsky, Mark.
Description:	XX, 201 p.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistical physics. -
Online resource:	https://doi.org/10.1007/978-3-319-04036-3
ISBN:	9783319040363

Dynamic Systems Models = New Methods of Parameter and State Estimation /
Boguslavskiy, Josif A.

Dynamic Systems ModelsNew Methods of Parameter and State Estimation /[electronic resource] :by Josif A. Boguslavskiy ; edited by Mark Borodovsky. - 1st ed. 2016. - XX, 201 p.online resource.

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.

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.

ISBN: 9783319040363

Standard No.: 10.1007/978-3-319-04036-3doiSubjects--Topical Terms:

528048
Statistical physics.

LC Class. No.: QC174.7-175.36

Dewey Class. No.: 621

Dynamic Systems Models = New Methods of Parameter and State Estimation /
LDR:03609nam a22004095i 4500 001 982596
003 DE-He213
005 20200702162339.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319040363 $9 978-3-319-04036-3
024 7 $a 10.1007/978-3-319-04036-3 $2 doi
035 $a 978-3-319-04036-3
050 4 $a QC174.7-175.36
072 7 $a PBWR $2 bicssc
072 7 $a SCI012000 $2 bisacsh
072 7 $a PBWR $2 thema
072 7 $a PHDT $2 thema
082 0 4 $a 621 $2 23
100 1 $a Boguslavskiy, Josif A. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1107222
245 1 0 $a Dynamic Systems Models $h [electronic resource] : $b New Methods of Parameter and State Estimation / $c by Josif A. Boguslavskiy ; edited by Mark Borodovsky.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a XX, 201 p. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 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.
520 $a 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.
650 0 $a Statistical physics. $3 528048
650 0 $a Mathematical models. $3 527886
650 0 $a Aerospace engineering. $3 686400
650 0 $a Astronautics. $3 646219
650 0 $a Signal processing. $3 561459
650 0 $a Image processing. $3 557495
650 0 $a Speech processing systems. $3 564428
650 0 $a Economics, Mathematical . $3 1253712
650 1 4 $a Applications of Nonlinear Dynamics and Chaos Theory. $3 1113607
650 2 4 $a Mathematical Modeling and Industrial Mathematics. $3 669172
650 2 4 $a Aerospace Technology and Astronautics. $3 683885
650 2 4 $a Signal, Image and Speech Processing. $3 670837
650 2 4 $a Quantitative Finance. $3 669372
700 1 $a Borodovsky, Mark. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 684740
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319040356
776 0 8 $i Printed edition: $z 9783319040370
776 0 8 $i Printed edition: $z 9783319791418
856 4 0 $u https://doi.org/10.1007/978-3-319-04036-3
912 $a ZDB-2-PHA
912 $a ZDB-2-SXP
950 $a Physics and Astronomy (SpringerNature-11651)
950 $a Physics and Astronomy (R0) (SpringerNature-43715)