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Vayssettes J.,Higher Institute of Aeronautics and Space | Mercere G.,CNRS Laboratory of Computer Science and Automatic Control Systems
Proceedings of the IEEE Conference on Decision and Control | Year: 2014

A new parametrisation of matrix fraction descriptions, named fully-parametrised left matrix fraction description (F-LMFD) is introduced in this article. This one contains ny2 over-parameters and consequently does not uniquely define a transfer function. Based on a study of the spanned equivalence class, local parametrisations of F-LMFD are then proposed to reduce the search space dimension when a gradient-based optimisation is performed. The formulation of the Gauss-Newton method is then considered and the new convergence scheme based on these local parametrisations is given. This one has a better numerical conditioning and is shown to avoid the numerical locking that can occurs with the conventional convergence schemes, based on minimal parametrisations of LMFD. The improvement of the convergence of the Gauss-Newton method is illustrated with the identification of a shaker. © 2014 IEEE. Source

Vayssettes J.,Higher Institute of Aeronautics and Space | Mercere G.,CNRS Laboratory of Computer Science and Automatic Control Systems | Prot O.,University of Limoges
Automatica | Year: 2016

This article aims at giving a new answer for the challenging problem of the parametrisation of multi-input multi-output matrix fraction descriptions. In order to reach this goal, new parametrisations of matrix fraction descriptions, called fully-parametrised left matrix fraction descriptions (F-LMFD), are first introduced. Their structural properties as well as their suitability for multi-input multi-output model description are more precisely analysed. As any over-parametrised model description, the F-LMFD cannot describe a transfer function uniquely. The structure of the space of equivalent F-LMFD is then investigated through the determination of its basis. The study carried out in this article is the prelude to a computational improvement of the identification of matrix fraction descriptions with gradient-based optimisation methods. © 2016 Elsevier Ltd. All rights reserved. Source

Vizer D.,Budapest University of Technology and Economics | Mercere G.,CNRS Laboratory of Computer Science and Automatic Control Systems
Periodica Polytechnica, Electrical Engineering | Year: 2014

When the identification of linear parameter-varying (LPV) models from local experiments is considered, the question of the necessary number of local operating points as well as the problem of the efficient interpolation of the locally-estimated linear time-invariant models arise. These challenging problems are tackled herein by using the H∞-norm. First, thanks to the nu-gap metric, an heuristic technique is introduced to optimize the number as well as the position of the local operating points (along a given trajectory of the scheduling variables) with respect to the information brought by the local models. Having access to a reliable set of local models, the second step of the procedure, i.e., the parameter estimation step, consists of the optimization a second H∞-norm-based cost function measuring the fit between the local information (represented by the locally-estimated LTI models) and the local behavior of a parameterized global LPV model. A special attention is given to parameterized LPV models satisfying a fully-parametrized or a physically-structured linear fractional representation. © 2014, Technical University of Budapest. All rights reserved. Source

Raddaoui B.,CNRS Laboratory of Computer Science and Automatic Control Systems | Samet A.,Tunis el Manar University
ICAART 2016 - Proceedings of the 8th International Conference on Agents and Artificial Intelligence | Year: 2016

Modern real-world applications are forced to deal with inconsistent, unreliable and imprecise information. In this setting, considerable research efforts have been put into the field of caring for the intrinsic imprecision of the data. Indeed, several frameworks have been introduced to deal with imperfection such as probabilistic, fuzzy, possibilistic and evidential databases. In this paper, we present an alternative framework, called correlated incomplete database, to deal with information suffering with imprecision. In addition, correlated incomplete database is studied from a data mining point of view. Since, frequent itemset mining is one of the most fundamental problems in data mining, we propose an algorithm to extract frequent patterns from correlated incomplete databases. Our experiments demonstrate the effectiveness and scalability of our framework. Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Source

Mercere G.,CNRS Laboratory of Computer Science and Automatic Control Systems | Prot O.,University of Limoges | Ramos J.A.,Nova Southeastern University
IEEE Transactions on Automatic Control | Year: 2014

While determining the order as well as the matrices of a black-box linear state-space model is now an easy problem to solve, it is well-known that the estimated (fully parameterized) state-space matrices are unique modulo a non-singular similarity transformation matrix. This could have serious consequences if the system being identified is a real physical system. Indeed, if the true model contains physical parameters, then the identified system could no longer have the physical parameters in a form that can be extracted easily. By assuming that the system has been identified consistently in a fully parameterized form, the question addressed in this paper then is how to recover the physical parameters from this initially estimated black-box form. Two solutions to solve such a parameterization problem are more precisely introduced. First, a solution based on a null-space-based reformulation of a set of equations arising from the aforementioned similarity transformation problem is considered. Second, an algorithm dedicated to nonsmooth optimization is presented to transform the initial fully parameterized model into the structured state-space parameterization of the system to be identified. A specific constraint on the similarity transformation between both system representations is added to avoid singularity. By assuming that the physical state-space form is identifiable and the initial fully parameterized model is consistent, it is proved that the global solutions of these two optimization problems are unique. The proposed algorithms are presented, along with an example of a physical system. © 2014 IEEE. Source

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