Dynamic Systems Models: New Methods of Parameter and State Estimation

Book Details

• Author: Josif A. Boguslavskiy

• Brand: Springer

• Edition: 1st Ed. 2016

• Binding: Hardcover

• ISBN: 9783319040356

• Pages: 201

• Release Date: 30-03-2016

• Languages: English

About The Book

Dynamic Systems Models by Josif A. Boguslavskiy presents an innovative approach to solving inverse problems using polynomial approximation methods. Designed for researchers in aerospace engineering, bioinformatics, financial mathematics, and related fields, this book introduces a method for parameter estimation of time series data derived from simulations of real experiments. These time series can arise from a wide range of scenarios such as analyzing aircraft flight dynamics, bioinformatics models like hidden Markov models, or the study of asset pricing through nonlinear financial models.

The monograph outlines algorithms based on polynomial approximation, which offer distinct advantages over traditional iterative methods. Unlike other methods, these algorithms do not require an initial approximation of a root vector and can handle non-differentiable elements in vector functions. This makes them more versatile and accessible for a broader range of applications.

With a focus on practical implementation, the book covers everything from the mathematical fundamentals of polynomial approximation to algorithm development, concluding with its application to real-world problems such as aeroplane flight dynamics and biological sequence analysis. Numerous worked examples and training techniques for the algorithms are provided, making it an invaluable resource for both theoretical study and applied research.

This work will be of particular interest to academic researchers working on inverse problems and their solutions, offering a reliable, efficient method for nonlinear estimation and control design in complex systems.