Network-Based Analysis of Dynamical Systems
Methods for Controllability and Observability Analysis, and Optimal Sensor Placement
(Sprache: Englisch)
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...
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Klappentext zu „Network-Based Analysis of Dynamical Systems “
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.
Inhaltsverzeichnis zu „Network-Based Analysis of Dynamical Systems “
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.Autoren-Porträt von Dániel Leitold, Ágnes Vathy-Fogarassy, János Abonyi
Dr. János Abonyi is a Full Professor of Computer Science and Chemical Engineering at the Department of Process Engineering, University of Pannonia, Veszprém, Hungary. His other publications include the Springer titles Interpretability of Computational Intelligence-Based Regression Models, and (with Dr. Vathy-Fogarassy) Graph-Based Clustering and Data Visualization Algorithms. Dr. Ágnes Vathy-Fogarassy is an Associate Professor at the Department of Computer Science and Systems Technology at the University of Pannonia.
Dániel Leitold is an Assistant Lecturer at the University of Pannonia.
Bibliographische Angaben
- Autoren: Dániel Leitold , Ágnes Vathy-Fogarassy , János Abonyi
- 2020, 1st ed. 2020, XIII, 110 Seiten, 43 farbige Abbildungen, Masse: 15,9 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3030364712
- ISBN-13: 9783030364717
Sprache:
Englisch
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