A comparison of techniques to get sparse rational approximations for linear fractional representations

Abstract : The objective of this paper is to stress that the size of a Linear Fractional Representation (LFR) significantly depends on the way tabulated or irrational data are approximated during the prior modeling process. It is notably shown that rational approximants can result in much smaller LFR than polynomial ones. Accor-dingly, 2 new methods are proposed to generate sparse rational models, which avoid data overfitting and lead to simple yet accurate LFR. The 1 st one builds a parsimonious modeling based on surrogate models and a new powerful global optimization method, and then translates the result into a fractional form. The 2 nd one looks for a rational approximant in a single step thanks to a symbolic regression technique, and relies on Genetic Programming to select sparse monomials. This work takes place in a more general project led by ONERA/DCSD and aimed at developing a Systems Modeling, Analysis and Control Toolbox (SMAC) for Matlab.
Document type :
Conference papers
Complete list of metadatas

Cited literature [39 references]  Display  Hide  Download

Contributor : Véronique Soullier <>
Submitted on : Friday, November 28, 2014 - 11:25:49 AM
Last modification on : Tuesday, March 26, 2019 - 2:28:03 PM
Long-term archiving on : Friday, April 14, 2017 - 10:48:22 PM


Publisher files allowed on an open archive


  • HAL Id : hal-01088599, version 1



C. Roos, G. Hardier, C. Döll. A comparison of techniques to get sparse rational approximations for linear fractional representations. 29th Congress of the International Council of the Aeronautical Sciences (ICAS 2014), Sep 2014, SAINT-PETERSBOURG, Russia. ⟨hal-01088599⟩



Record views


Files downloads