Dimensionless systems identification and model order reduction

Author(s):  
Romero ◽  
Alfaro ◽  
Arrieta
2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Christophe Corbier ◽  
Jean-Claude Carmona

A new family of MLE typeLpestimators for model order reduction in dynamical systems identification is presented in this paper. A family ofLpdistributions proposed in this work combinesLp2(1<p2<2) andLp1(0<p1<1) distributions which are quantified by four parameters. The main purpose is to show that these parameters add degrees of freedom (DOF) in the estimation criterion and reduce the estimated model complexity. Convergence consistency properties of the estimator are analysed and the model order reduction is established. Experimental results are presented and discussed on a real vibration complex dynamical system and pseudo-linear models are considered.


Author(s):  
Vladimir Lantsov ◽  
A. Papulina

The new algorithm of solving harmonic balance equations which used in electronic CAD systems is presented. The new algorithm is based on implementation to harmonic balance equations the ideas of model order reduction methods. This algorithm allows significantly reduce the size of memory for storing of model equations and reduce of computational costs.


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