Model order reduction of random parameter-dependent linear systems

Automatica ◽  
2015 ◽  
Vol 55 ◽  
pp. 95-107 ◽  
Author(s):  
Lyès Nechak ◽  
Henri-François Raynaud ◽  
Caroline Kulcsár
2012 ◽  
Vol 43 (9) ◽  
pp. 1753-1763 ◽  
Author(s):  
Abderazik Birouche ◽  
Benjamin Mourllion ◽  
Michel Basset

2018 ◽  
Vol 18 (04) ◽  
pp. 1850033 ◽  
Author(s):  
Martin Redmann ◽  
Peter Benner

To solve a stochastic linear evolution equation numerically, finite dimensional approximations are commonly used. For a good approximation, one might end up with a sequence of ordinary stochastic linear equations of high order. To reduce the high dimension for practical computations, we consider the singular perturbation approximation as a model order reduction technique in this paper. This approach is well-known from deterministic control theory and here we generalize it for controlled linear systems with Lévy noise. Additionally, we discuss properties of the reduced order model, provide an error bound, and give some examples to demonstrate the quality of this model order reduction technique.


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