Time-Variant and Quasi-separable Systems - Dewilde, Patrick; Diepold, Klaus; Van der Veen, Alle-Jan; - Prospero Internet Bookshop

Time-Variant and Quasi-separable Systems: Matrix Theory, Recursions and Computations
 
Product details:

ISBN13:9781009455626
ISBN10:1009455621
Binding:Hardback
No. of pages:330 pages
Size:244x170x19 mm
Weight:772 g
Language:English
700
Category:

Time-Variant and Quasi-separable Systems

Matrix Theory, Recursions and Computations
 
Publisher: Cambridge University Press
Date of Publication:
 
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GBP 74.99
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Short description:

Bringing together systems theory, signal processing and numerical computation, this book presents an appealing novel and combined approach.

Long description:
Matrix theory is the lingua franca of everyone who deals with dynamically evolving systems, and familiarity with efficient matrix computations is an essential part of the modern curriculum in dynamical systems and associated computation. This is a master's-level textbook on dynamical systems and computational matrix algebra. It is based on the remarkable identity of these two disciplines in the context of linear, time-variant, discrete-time systems and their algebraic equivalent, quasi-separable systems. The authors' approach provides a single, transparent framework that yields simple derivations of basic notions, as well as new and fundamental results such as constrained model reduction, matrix interpolation theory and scattering theory. This book outlines all the fundamental concepts that allow readers to develop the resulting recursive computational schemes needed to solve practical problems. An ideal treatment for graduate students and academics in electrical and computer engineering, computer science and applied mathematics.

'This book represents the first comprehensive single-volume coverage on signal processing, dynamical systems and numerical algorithms. It will be a timely reference for students, practitioners, and researchers in the areas of systems, control, estimation, identification, optimization and modern data sciences - since math is the cornerstone of AI. Sun-Yuan Kung, Princeton University
Table of Contents:
Part I. Lectures on Basics, with Examples: 1. A first example: optimal quadratic control; 2. Dynamical systems; 3. LTV (quasi-separable) systems; 4. System identification; 5. State equivalence, state reduction; 6. Elementary operations; 7. Inner operators and external factorizations; 8. Inner-outer factorization; 9. The Kalman filter as an application; 10. Polynomial representations; 11. Quasi-separable Moore-Penrose inversion; Part II. Further Contributions to Matrix Theory: 12. LU (spectral) factorization; 13. Matrix Schur interpolation; 14. The scattering picture; 15. Constrained interpolation; 16. Constrained model reduction; 17. Isometric embedding for causal contractions; Appendix. Data model and implementations; References; Index.