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Network-based analysis of dynamical ...

Leitold, Daniel.

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Network-based analysis of dynamical systems = methods for controllability and observability analysis, and optimal sensor placement /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Network-based analysis of dynamical systems/ by Daniel Leitold, Agnes Vathy-Fogarassy, Janos Abonyi.
Reminder of title:	methods for controllability and observability analysis, and optimal sensor placement /
Author:	Leitold, Daniel.
other author:	Vathy-Fogarassy, Agnes.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xiii, 110 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Structural Controllability and Observability Analysis in Complex Networks -- 3. Reduction of Relative Degree by Optimal Control and Sensor Placement -- 4. Applications to the analysis of Heat Exchanger Networks -- 5. The NOCAD MATLAB/Octave Toolbox Developed for the Analysis of Dynamical Systems -- 6. Summary.
Contained By:	Springer eBooks
Subject:	System analysis - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-030-36472-4
ISBN:	9783030364724

Network-based analysis of dynamical systems = methods for controllability and observability analysis, and optimal sensor placement /
Leitold, Daniel.

Network-based analysis of dynamical systemsmethods for controllability and observability analysis, and optimal sensor placement /[electronic resource] :by Daniel Leitold, Agnes Vathy-Fogarassy, Janos Abonyi. - Cham :Springer International Publishing :2020. - xiii, 110 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

1. Introduction -- 2. Structural Controllability and Observability Analysis in Complex Networks -- 3. Reduction of Relative Degree by Optimal Control and Sensor Placement -- 4. Applications to the analysis of Heat Exchanger Networks -- 5. The NOCAD MATLAB/Octave Toolbox Developed for the Analysis of Dynamical Systems -- 6. Summary.

This book explores the key idea that the dynamical properties of complex systems can be determined by effectively calculating specific structural features using network science-based analysis. Furthermore, it argues that certain dynamical behaviours can stem from the existence of specific motifs in the network representation. Over the last decade, network science has become a widely applied methodology for the analysis of dynamical systems. Representing the system as a mathematical graph allows several network-based methods to be applied, and centrality and clustering measures to be calculated in order to characterise and describe the behaviours of dynamical systems. The applicability of the algorithms developed here is presented in the form of well-known benchmark examples. The algorithms are supported by more than 50 figures and more than 170 references; taken together, they provide a good overview of the current state of network science-based analysis of dynamical systems, and suggest further reading material for researchers and students alike. The files for the proposed toolbox can be downloaded from a corresponding website.

ISBN: 9783030364724

Standard No.: 10.1007/978-3-030-36472-4doiSubjects--Topical Terms:

587843
System analysis
--Mathematical models.

LC Class. No.: QA402 / .L45 2020

Dewey Class. No.: 004.21

Network-based analysis of dynamical systems = methods for controllability and observability analysis, and optimal sensor placement /
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245 1 0 $a Network-based analysis of dynamical systems $h [electronic resource] : $b methods for controllability and observability analysis, and optimal sensor placement / $c by Daniel Leitold, Agnes Vathy-Fogarassy, Janos Abonyi.
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505 0 $a 1. Introduction -- 2. Structural Controllability and Observability Analysis in Complex Networks -- 3. Reduction of Relative Degree by Optimal Control and Sensor Placement -- 4. Applications to the analysis of Heat Exchanger Networks -- 5. The NOCAD MATLAB/Octave Toolbox Developed for the Analysis of Dynamical Systems -- 6. Summary.
520 $a This book explores the key idea that the dynamical properties of complex systems can be determined by effectively calculating specific structural features using network science-based analysis. Furthermore, it argues that certain dynamical behaviours can stem from the existence of specific motifs in the network representation. Over the last decade, network science has become a widely applied methodology for the analysis of dynamical systems. Representing the system as a mathematical graph allows several network-based methods to be applied, and centrality and clustering measures to be calculated in order to characterise and describe the behaviours of dynamical systems. The applicability of the algorithms developed here is presented in the form of well-known benchmark examples. The algorithms are supported by more than 50 figures and more than 170 references; taken together, they provide a good overview of the current state of network science-based analysis of dynamical systems, and suggest further reading material for researchers and students alike. The files for the proposed toolbox can be downloaded from a corresponding website.
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