quartonews.it » Math and Science » Approximation of Large-Scale Dynamical Systems (Advances in Design and Control)

Author: | Athanasios C. Antoulas |

Subcategory: | Mathematics |

Language: | English |

Publisher: | Society for Industrial and Applied Mathematics; Reprint edition (February 15, 2010) |

Pages: | 510 pages |

Category: | Math and Science |

Rating: | 4.3 |

Other formats: | lrf docx azw doc |

In this book he clearly differentiated Singular Value Decomposition (SVD) methods and Krylov subspace projection methods and their explanation in part III and IV is excellent. The fist two chapter of introduction and preliminaries give you sufficient background to understand Part III and IV. The book does require some familiarity with numerical linear algebra and may be difficult for a new comer in the area to follow, however the way it is written it can be used both by engineers and scientist.

Approximation of Large-Sc. has been added to your Cart. In this book he clearly differentiated Singular Value Decomposition (SVD) methods and Krylov subspace projection methods and their explanation in part III and IV is excellent.

This book is for anyone interested in model reduction.

Approximation of Large-Scale Dynamical Systems provides a comprehensive picture of model reduction, combining system theory with numerical linear algebra and computational considerations. It addresses the issue of model reduction and the resulting trade-offs between accuracy and complexity. This book is for anyone interested in model reduction. Contents List of Figures; Foreword; Preface; How to Use this Book; Part I: Introduction.

Approximation of Large-Scale Dynamical Systems (Advances in Design and Control). Athanasios C. Antoulas. Download (pdf, 2. 2 Mb) Donate Read.

Thanos Antoulas was born in Athens, Greece. Thanos Antoulas was born in Athens, Greece. He studied at the ETH Zürich, where he obtained the Diploma of Electrical Engineering in 1975, the Diploma of Mathematics in 1975, and the P. degree in Mathematics in 1980.

Antoulas, Athanasios C. Antoulas, Athanasios .

Mathematical models are used to simulate, and sometimes control, the behavior of physical and artificial processes such as the weather and very large-scale integration (VLSI) circuits. The increasing need for accuracy has led to the development of highly complex models

A Systems-Theoretic Security Model for Large Scale, Complex Systems Applied to the US Air Transportation System.

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Автор: Antoulas, Athanasios C. Название: Approximation of large-scale dynamical systems Издательство .

He has written numerous books and articles on virtually all aspects of stochastic systems theory, and has received various awards including the IEEE Control Systems Field Award.

oceedings{ationOL, title {Approximation of Large-Scale Dynamical Systems}, author {Athanasios C. Antoulas}, booktitle {Advances in Design and Control}, year {2005} }.

In simulation (control) one seeks to predict (modify) the system behavior .

In simulation (control) one seeks to predict (modify) the system behavior; however, simulation of the full model is often not feasible, necessitating simplification of it. Due to limited computational, accuracy, and storage capabilities, system approximation-the development of simplified models that capture the main features of the original dynamical systems-evolved. Large-scale simulations play a crucial role in the study of a great variety of complex physical phenomena, leading often to overwhelming demands on computational resources.

Mathematical models are used to simulate, and sometimes control, the behavior of physical and artificial processes such as the weather and very large-scale integration (VLSI) circuits. The increasing need for accuracy has led to the development of highly complex models. However, in the presence of limited computational accuracy and storage capabilities model reduction (system approximation) is often necessary. Approximation of Large-Scale Dynamical Systems provides a comprehensive picture of model reduction, combining system theory with numerical linear algebra and computational considerations. It addresses the issue of model reduction and the resulting trade-offs between accuracy and complexity. Special attention is given to numerical aspects, simulation questions, and practical applications.

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