This book presents a novel approach to the theory of dynamical systems using computational matrix algebra. It provides everything necessary to allow readers to develop recursive computational schemes needed to solve practical problems. Ideal for graduate students in electrical and computer engineering, computer science and applied mathematics.
This book presents a novel approach to the theory of dynamical systems using computational matrix algebra. It provides everything necessary to allow readers to develop recursive computational schemes needed to solve practical problems. Ideal for graduate students in electrical and computer engineering, computer science and applied mathematics.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Patrick Dewilde was a professor at the Delft University of Technology for 31 years and previously Director of the Delft Institute for Microelectronics, Chairman of a major Dutch research funding agency and Director of the Institute of Advanced Study of the Technical University of Munich. He is an Institute of Electrical and Electronics Engineers (IEEE) Fellow, a winner of the IEEE Belevitch Award and an elected member of the Dutch Royal Academy of Arts and Science.
Inhaltsangabe
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.
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.
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