DocumentCode :
188778
Title :
Model reduction for complex hyperbolic networks
Author :
Himpe, Christian ; Ohlberger, Mario
Author_Institution :
Inst. for Comput. & Appl. Math., Univ. of Munster, Munster, Germany
fYear :
2014
fDate :
24-27 June 2014
Firstpage :
2739
Lastpage :
2743
Abstract :
We recently introduced the joint gramian for combined state and parameter reduction [C. Himpe and M. Ohlberger. Cross-Gramian-Based Combined State and Parameter Reduction for Large-Scale Control Systems. arXiv:1302.0634, 2013], which is applied in this work to reduce a parametrized linear time-varying control system modeling a hyperbolic network. The reduction encompasses the dimension of nodes and parameters of the underlying control system. Networks with a hyperbolic structure have many applications as models for large-scale systems. A prominent example is the brain, for which a network structure of the various regions is often assumed to model propagation of information. Networks with many nodes, and parametrized, uncertain or even unknown connectivity require many and individually computationally costly simulations. The presented model order reduction enables vast simulations of surrogate networks exhibiting almost the same dynamics with a small error compared to full order model.
Keywords :
complex networks; large-scale systems; nonlinear control systems; reduced order systems; time-varying systems; combined state-parameter reduction; complex hyperbolic networks; full order model; hyperbolic structure; large-scale systems; model reduction; network structure; parametrized linear time-varying control system; surrogate network simulations; Brain modeling; Computational modeling; Control systems; Joints; Observability; Reduced order systems; Symmetric matrices; Combined Reduction; Controllability; Cross Gramian; Empirical Gramian; Model Reduction; Observability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (ECC), 2014 European
Conference_Location :
Strasbourg
Print_ISBN :
978-3-9524269-1-3
Type :
conf
DOI :
10.1109/ECC.2014.6862188
Filename :
6862188
Link To Document :
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