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The Resource Dynamic Systems Models : New Methods of Parameter and State Estimation, by Josif A. Boguslavskiy ; edited by Mark Borodovsky, (electronic resource)
Dynamic Systems Models : New Methods of Parameter and State Estimation, by Josif A. Boguslavskiy ; edited by Mark Borodovsky, (electronic resource)
Resource Information
The item Dynamic Systems Models : New Methods of Parameter and State Estimation, by Josif A. Boguslavskiy ; edited by Mark Borodovsky, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.This item is available to borrow from all library branches.
Resource Information
The item Dynamic Systems Models : New Methods of Parameter and State Estimation, by Josif A. Boguslavskiy ; edited by Mark Borodovsky, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.
This item is available to borrow from all library branches.
 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 alreadypopular iterative methods. Specifically, they do not require a first approximation of a root vector and they allow nondifferentiable 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
 Language
 eng
 Edition
 1st ed. 2016.
 Extent
 XX, 201 p.
 Contents

 From the Contents: Linear Estimators of a RandomParameter 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
 Isbn
 9783319040363
 Label
 Dynamic Systems Models : New Methods of Parameter and State Estimation
 Title
 Dynamic Systems Models
 Title remainder
 New Methods of Parameter and State Estimation
 Statement of responsibility
 by Josif A. Boguslavskiy ; edited by Mark Borodovsky
 Subject

 Mathematical Modeling and Industrial Mathematics
 Economics, Mathematical
 Aerospace engineering
 Economics, Mathematical
 Physics
 Statistical physics
 Mathematical models
 Nonlinear Dynamics
 Aerospace engineering
 Aerospace Technology and Astronautics
 Aerospace engineering
 Mathematical models
 Electronic resources
 Quantitative Finance
 Physics
 Signal, Image and Speech Processing
 Statistical physics
 Astronautics
 Astronautics
 Physics
 Language
 eng
 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 alreadypopular iterative methods. Specifically, they do not require a first approximation of a root vector and they allow nondifferentiable 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
 http://library.link/vocab/creatorName
 Boguslavskiy, Josif A
 Image bit depth
 0
 LC call number
 QC174.7175.36
 Literary form
 non fiction
 http://library.link/vocab/relatedWorkOrContributorName

 Borodovsky, Mark.
 SpringerLink
 http://library.link/vocab/subjectName

 Physics
 Economics, Mathematical
 Mathematical models
 Statistical physics
 Aerospace engineering
 Astronautics
 Physics
 Nonlinear Dynamics
 Mathematical Modeling and Industrial Mathematics
 Aerospace Technology and Astronautics
 Signal, Image and Speech Processing
 Quantitative Finance
 Label
 Dynamic Systems Models : New Methods of Parameter and State Estimation, by Josif A. Boguslavskiy ; edited by Mark Borodovsky, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 From the Contents: Linear Estimators of a RandomParameter 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
 Dimensions
 unknown
 Edition
 1st ed. 2016.
 Extent
 XX, 201 p.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9783319040363
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9783319040363
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783319040363
 Label
 Dynamic Systems Models : New Methods of Parameter and State Estimation, by Josif A. Boguslavskiy ; edited by Mark Borodovsky, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 From the Contents: Linear Estimators of a RandomParameter 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
 Dimensions
 unknown
 Edition
 1st ed. 2016.
 Extent
 XX, 201 p.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9783319040363
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9783319040363
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783319040363
Subject
 Aerospace Technology and Astronautics
 Aerospace engineering
 Aerospace engineering
 Aerospace engineering
 Astronautics
 Astronautics
 Economics, Mathematical
 Economics, Mathematical
 Electronic resources
 Mathematical Modeling and Industrial Mathematics
 Mathematical models
 Mathematical models
 Nonlinear Dynamics
 Physics
 Physics
 Physics
 Quantitative Finance
 Signal, Image and Speech Processing
 Statistical physics
 Statistical physics
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