Marion Gilson

Bio: Marion Gilson is an academic researcher from University of Lorraine. The author has contributed to research in topics: System identification & Instrumental variable. The author has an hindex of 22, co-authored 100 publications receiving 1650 citations. Previous affiliations of Marion Gilson include Centre national de la recherche scientifique & GlaxoSmithKline.

Optimal instrumental variable method for closed-loop identification

Abstract: This study presents in a new unified way, optimal instrumental variable methods for identifying discrete-time transfer function models when the system operates in a closed loop. The conditions for the optimal design of prefilters and instruments depending on common model structures are analysed and different approaches are developed according to whether the controller is known or not. The performance of the proposed approaches is evaluated by Monte-Carlo analysis in comparison with other alternative closed-loop estimation methods.

Refined Instrumental Variable Identification of Continuous-time Hybrid Box-Jenkins Models

Abstract: This chapter describes and evaluates a statistically optimal method for the identification and estimation3 of continuous-time (CT) hybrid Box-Jenkins (BJ) transfer function models from discrete-time, sampled data. Here, the model of the basic dynamic system is estimated in continuous-time, differential equation form, while the associated additive noise model is estimated as a discrete-time, autoregressive moving average (ARMA) process. This refined instrumental variable method for continuous-time systems (RIVC) was first developed in 1980 by Young and Jakeman [52] and its simplest embodiment, the simplified RIVC (SRIVC) method, has been used successfully for many years, demonstrating the advantages that this stochastic formulation of the continuous-time estimation problem provides in practical applications (see, e.g., some recent such examples in [16, 34, 40, 45, 48]).

Subspace model identification-Part 1. The output-error state-space model identification class of algorithms

Abstract: In this paper, we present two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data. The algorithms have a number of common features. They are classified as one of the subspace model identification schemes, in that a major part of the identification problem consists of calculating specially structured subspaces of spaces defined by the input-output data. This structure is then exploited in the calculation of a realization. Another common feature is their algorithmic organization: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set (or sets) of equations. The schemes assume that the underlying system has an output-error structure and that a measurable input sequence is available. The latter characteristic indicates that both schemes are versions of the MIMO Output-Error State Space model identification (MOESP) approach. The first algorithm is denoted in particular as the (elementary MOESP scheme)...

Identification of Continuous-time Models from Sampled Data

Abstract: System identification is an established field in the area of system analysis and control. It aims to determine particular models for dynamical systems based on observed inputs and outputs. Although dynamical systems in the physical world are naturally described in the continuous-time domain, most system identification schemes have been based on discrete-time models without concern for the merits of natural continuous-time model descriptions. The continuous-time nature of physical laws, the persistent popularity of predominantly continuous-time proportional-integral-derivative control and the more direct nature of continuous-time fault diagnosis methods make continuous-time modeling of ongoing importance. Identification of Continuous-time Models from Sampled Data brings together contributions from well-known experts who present an up-to-date view of this active area of research and describe recent methods and software tools developed in this field. They offer a fresh look at and new results in areas such as: time and frequency domain optimal statistical approaches to identification; parametric identification for linear, nonlinear and stochastic systems; identification using instrumental variable, subspace and data compression methods; closed-loop and robust identification; and continuous-time modeling from non-uniformly sampled data and for systems with delay. The CONtinuous-Time System IDentification (CONTSID) toolbox described in the book gives an overview of developments and practical examples in which MATLAB can be brought to bear in the cause of direct time-domain identification of continuous-time systems.This survey of methods and results in continuous-time system identification will be a valuable reference for a broad audience drawn from researchers and graduate students in signal processing as well as in systems and control. It also covers comprehensive material suitable for specialised graduate courses in these areas.

Nonlinear System Identification

Abstract: This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises

Errors-in-Variables Methods in System Identification

Abstract: The paper gives a survey of errors-in-variables methods in system identification. Background and motivation are given, and examples illustrate why the identification problem can be difficult. Under general weak assumptions, the systems are not identifiable, but can be parameterized using one degree-of-freedom. Examples where identifiability is achieved under additional assumptions are also provided. A number of approaches for parameter estimation of errors-in-variables models are presented. The underlying assumptions and principles for each approach are highlighted.