1,011 research outputs found
A bilinear differential forms approach to parametric structured state-space modelling
We use one-variable Loewner techniques to compute polynomial-parametric models for MIMO systems from vector-exponential data gathered at various points in the parameter space. Instrumental in our approach are the connections between vector-exponential modelling via bilinear differential forms and the Loewner framework
Cointegration Analysis with State Space Models
This paper presents and exemplifies results developed for cointegration analysis with state space models by Bauer and Wagner in a series of papers. Unit root processes, cointegration and polynomial cointegration are defined. Based upon these definitions the major part of the paper discusses how state space models, which are equivalent to VARMA models, can be fruitfully employed for cointegration analysis. By means of detailing the cases most relevant for empirical applications, the I(1), MFI(1) and I(2) cases, a canonical representation is developed and thereafter some available statistical results are briefly mentioned.State space models, unit roots, cointegration, polynomial cointegration, pseudo maximum likelihood estimation, subspace algorithms
Pseudo-Optimal Reduction of Structured DAEs by Rational Interpolation
In this contribution, we extend the concept of inner product
and pseudo-optimality to dynamical systems modeled by
differential-algebraic equations (DAEs). To this end, we derive projected
Sylvester equations that characterize the inner product in
terms of the matrices of the DAE realization. Using this result, we extend the
pseudo-optimal rational Krylov algorithm for ordinary
differential equations to the DAE case. This algorithm computes the globally
optimal reduced-order model for a given subspace of defined by
poles and input residual directions. Necessary and sufficient conditions for
pseudo-optimality are derived using the new formulation of the
inner product in terms of tangential interpolation conditions.
Based on these conditions, the cumulative reduction procedure combined with the
adaptive rational Krylov algorithm, known as CUREd SPARK, is extended to DAEs.
Important properties of this procedure are that it guarantees stability
preservation and adaptively selects interpolation frequencies and reduced
order. Numerical examples are used to illustrate the theoretical discussion.
Even though the results apply in theory to general DAEs, special structures
will be exploited for numerically efficient implementations
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