Authors: Wingerden, van, Jan-Willem; Verhaegen, Michel

Abstract: In this paper we present an algorithm to identify MIMO Hammerstein systems under open and closed-loop conditions. To do so, we formulate the optimized predictor based subspace identification algorithm in the dual space. In this dual space we utilize ideas from support vector machines to estimate the state sequence. With the state sequence known, we use the same machinery to estimate the system matrices and the static nonlinearity. The effectiveness of the approach is illustrated with a closed-loop simulation example.

Keywords: Nonlinear System Identification; Subspace Methods; Closed Loop Identification

Identifier: 10.3182/20090706-3-FR-2004.00274

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Hildebrand, Roland; Solari, Gabriel

Abstract: We consider optimal experiment design for parametric prediction error system identification of linear time-invariant multi-input multi-output systems in closed-loop. The optimization is performed jointly over the controller and the external input. We use a partial correlation approach, i.e. parametrize the set of admissible controller - external input pairs by a finite set of matrix-valued trigonometric moments. Our main contribution is to derive a description of the set of admissible finite-dimensional moment vectors by a linear matrix inequality. Optimal input design problems with constraints and criteria which are linear in these moments can then be cast as semi-definite programs and solved by standard semi-definite programming packages. Our results can be applied to most of the usual model structures, but we assume that the true system is in the model set.

Keywords: Input and Excitation Design; Closed Loop Identification; Multivariable System Identification

Identifier: 10.3182/20090706-3-FR-2004.00154

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Lemos, Joao; Oliveira, Pedro; Marques, Jorge S.; Mendonça, Teresa

Abstract: This work approaches the problem of forming clusters of linearized models that cover the possible dynamic behavior of neuromuscular blockade of patients subject to anaesthesia. The motivation stands from the design of supervised multi-model adaptive controllers where a group of "similar" models is associated with a single controller. Due to this connection to a control problem, the similarity among models is measured using the $nu-gap$ metric since this ensures that, if two models are close in this norm, then a controller that stabilizes one of the models will also stabilize the other. Two algorithms are proposed for model clustering. The first one starts with an initial classification that relies on insight from the particular problem of neuromuscular blockade control. The other may be used in other applications as well and relies on an initialization based on agglomerative clustering techniques. In both cases, the initial classification is then improved by the $k-means$ algorithm.

Keywords: Identification for Control; Biological Systems; Model Validation

Identifier: 10.3182/20090706-3-FR-2004.00166

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Mogensen, Lars V.; Hansen, Søren; Ravn, Ole; Poulsen, Niels Kjølstad

Abstract: In this paper we present an estimation platform with simulation capabilities to evaluate methods for localisation of a mobile robot using a feature map. The platform is based on the Kalmtool 4 toolbox which is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox contains functions for extended Kalman filtering as well as for DD1 filter and the DD2 filter. It also contains functions for Unscented Kalman filters as well as three versions of particle filters. The toolbox requires MATLAB version 7, but no additional toolboxes are required.

Keywords: Toolboxes; Bayesian Methods

Identifier: 10.3182/20090706-3-FR-2004.00085

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Chitralekha, Saneej; Prakash, Jagadeesan; Raghavan, Harigopal; Gopaluni, Ratna Bhushan; Shah, Sirish

Abstract: Abstract: The primary requirement of filtering algorithms such as Particle Filter (PF), Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) is the availability of an accurate nonlinear state space model. In the absence of good parameter values, one has to estimate both the hidden states and the unknown parameters in a joint framework using measurements available from the process. This problem of joint state and parameter estimation for nonlinear systems can be solved recursively through the combination of a nonlinear smoother and a maximum likelihood parameter estimation scheme. Expectation Maximization (EM) is an efficient optimization algorithm which can provide the maximum likelihood estimate of the model parameters even in the presence of missing data. The algorithm can generate parameter estimates that maximize the likelihood of all the data including those with missing output measurements. This paper presents an approach which combines the EM algorithm with a suitable nonlinear smoother, such as PF, UKF or EKF based smoother. An application of this method to a simulated Continuous Fermentor process with unknown model parameters is presented. A comparative study of the results, when the different smoothing schemes were used in this approach, is presented. The results show that the UKS based technique was able to generate unbiased parameter estimates. The Particle Smoother based parameter estimates converged in the neighbouhood of their true values, but the technique was found to be computationally intensive compared to the UKS and EKS based techniques.

Keywords: Nonlinear System Identification; Filtering and Smoothing

Identifier: 10.3182/20090706-3-FR-2004.00133

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Balsa-Canto, Eva; Banga, Julio R.

Abstract: The nonlinear character and the usually large number of parameters in biological mathematical models make model identification from experimental data a rather complex task. The origin of such complexity is often related with the lack of identifiability. This work presents a model identification procedure and the corresponding numerical techniques to iteratively improve model predictive capabilities in the context of systems biology. The procedure involves several steps such as identifiability analysis, global ranking of parameters, parameter estimation and optimal experimental design. Most of these steps are being incorporated in a MATLAB based toolbox, AMIGO (Advanced Model Identification using Global Optimization). To illustrate the performance of the proposed procedure we considered a mathematical model that describes the NF-kappaB regulatory module involving several unknown parameters.

Keywords: Nonlinear System Identification; Identifiability; Biological Systems

Identifier: 10.3182/20090706-3-FR-2004.00207

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Zou, Yiqun; Heath, William Paul

Abstract: Maximum likelihood estimation(MLE) is a popular technique in both open and closed loop identification. However when the landscape of likelihood function has several local minima, gradient based optimization might end up with local convergence. To avoid this, various non-local-minimum conditions are derived in this paper. Here we consider different model structures, in particular Output-Error, ARMAX, and Box-Jenkins models.

Keywords: Maximum Likelihood Methods

Identifier: 10.3182/20090706-3-FR-2004.00184

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Gevers, Michel; Bazanella, Alexandre S.; Bombois, Xavier

Abstract: This paper establishes, in a Prediction Error Identification (PEI) context, the connections that exist between the identifiability of the model structure, the informativity of the data, the information matrix and the existence of a unique global minimum of the PEI criterion. By introducing the concept of informative data at a particular parameter value, we are able to establish a number of equivalences and connections between these four ingredients of the identification problem, for both open-loop and closed-loop identification.

Keywords: Identifiability; Input and Excitation Design

Identifier: 10.3182/20090706-3-FR-2004.00112

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Juillard, Jerome; Jafaridinani, Kian; Colinet, Eric

Abstract: In this paper, we present a new weighted least-squares (WLS) approach for parameter estimation based on binary data. Two WLS criteria are studied. We show that these two criteria do not have the same asymptotical behavior although they are closely related. Particularly, in the presence of noise, one of the criteria used for determining the system parameters provides an appropriate estimation, whereas the other one leads to an underestimation of the system parameters. These asymptotical results are illustrated by simulations in Gaussian and non-Gaussian contexts.

Keywords: Nonlinear System Identification; Identification for Control

Identifier: 10.3182/20090706-3-FR-2004.00011

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Rojas, Cristian; Hjalmarsson, Håkan; Gerencser, Laszlo; Mårtensson, Jonas

Abstract: In this contribution we show that under certain conditions it is possible to estimate a non-minimum phase zero consistently using a very simple 2 parameter finite impulse response model, for arbitrarily complex finite dimensional stable linear time invariant systems.

Keywords: Input and Excitation Design; Identification for Control; Recursive Identification

Identifier: 10.3182/20090706-3-FR-2004.00153

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Johansson, Rolf

Abstract: This paper presents theory, algorithms and validation results for system identification of continuous-time state-space models from finite non-uniformly sampled input-output sequences. The algorithms developed are methods of model identification and stochastic realization adapted to the continuous-time model context using non-uniformly sampled input-output data. The resulting model can be decomposed into an input-output model and a stochastic innovations model. For state estimation dynamics, we have designed a procedure to provide separate continuous-time temporal update and error-feedback update based on non-uniformly sampled input-output data. Stochastic onvergence analysis is provided.

Keywords: Continuous Time System Estimation; Recursive Identification; Time Series

Identifier: 10.3182/20090706-3-FR-2004.00193

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Narang, Anuj; Shah, Sirish; Chen, Tongwen

Abstract: This paper deals with the continuous-time model identi¯cation (CMI) of fractional order systems with time delays. In this paper, a new linear ¯lter is introduced for simultaneous estimation of all model parameters for commensurate fractional order systems with time delays (CFOTDS) based on step response data. The proposed method simultaneously estimates the time delay along with other model parameters in an iterative manner by solving simple linear regression equations. For the case when the fractional order is unknown, we also propose a nested loop optimization method where the time delay along with other model parameters are estimated iteratively in the inner loop and the fractional order is estimated in the non-linear outer loop. The applicability of the developed procedure is demonstrated on two fractal systems by doing Monte Carlo simulation analysis in the presence of white noise.

Keywords: Continuous Time System Estimation; Process Control

Identifier: 10.3182/20090706-3-FR-2004.00152

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Escobar, Jesica Azucena; Poznyak, Alexander S.

Abstract: This paper deals with time problem of time-varying parameters estimation of stochastic systems under colored noise perturbations. These perturbations have a standard "white noise" in the input of a forming filter which is assumed to be partially known (a nominal plant plus a bounded deviations). A two step method is proposed. First, it is designed a tracking process, based in the "equivalent control" technique, providing the finite-time equivalence of the origin stochastic process with unknown parameters to an auxiliary one. This step does not eliminate the noise, but it permits (at a short enough time) to represent the model to be identified in the, so-called, "regression form" and, at the same time, to realize the "semi-whitening" of noise keeping bounded uncertainties as an external unmeasured dynamics. In the second step the Least Squares Method (LSM) with a scalar forgetting factor is applied to estimate time varying parameters of the given model. The convergence zone analysis is presented. A numerical example illustrates the effectiveness of the proposed approach.

Keywords: Continuous Time System Estimation; Multivariable System Identification

Identifier: 10.3182/20090706-3-FR-2004.00064

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Welsh, James; Aguero, Juan C; Alamir, Mazen

Abstract: Identification of continuous-time systems typically present problems due to the facts that one cannot, in general, measure the time derivatives of the signals and, also, the sampled nature of the data. We utilise indirect inference as the underlying principle for continuous time system identification. Indirect inference has been widely used in the econometrics area for time series modeling. Here we adapt the indirect inference technique to include systems with an exogenous input and apply it to the problem of system identification. We use an example problem posed by Rao and Garnier to show the effectiveness of the indirect inference technique when contrasted to other continuous-time methods of identification.

Keywords: Continuous Time System Estimation

Identifier: 10.3182/20090706-3-FR-2004.00194

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Tanelli, Mara; Corno, Matteo; De Filippi, Pierpaolo; Rossi, Stefano; Savaresi, Sergio; Fabbri, Luca

Abstract: Recent technology advances in the field of semi-active steering dampers for motorcycles open the way to the design of innovative control strategies to improve two-wheeled vehicles stability. As such, it is of growing importance to devise simple and effective dynamical models of the bike steer dynamics to be employed for control design purposes. This paper proposes an analytical model of a two-wheeled vehicle tuned to capture the weave and wobble modes, which are the most representative to study steering related instabilities. The model is derived from first principles and its parameters tuned to to fit a hypersport motorcycle based on a grey-box identification procedure. The model effectiveness is assessed on data collected both from a full-fledged multi-body simulator and an instrumented vehicle.

Keywords: Mechanical and Aerospace; Grey Box Modelling

Identifier: 10.3182/20090706-3-FR-2004.00077

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Ucinski, Dariusz

Abstract: A method is developed to solve an optimal node activation problem in sensor networks whose measurements are supposed to be used to estimate unknown parameters of the underlying process model in the form of a partial differential equation. Given a partition of the observation horizon into a finite number of consecutive intervals, the problem is set up to select nodes which will be active over each interval while the others will remain dormant such that the log-determinant of the resulting Fisher information matrix associated with the estimated parameters is maximized. The search for the optimal solution is performed using the branch-and-bound method in which an extremely simple and efficient technique is employed to produce an upper bound to the maximum objective function.

Keywords: Continuous Time System Estimation; Error Quantification; Input and Excitation Design

Identifier: 10.3182/20090706-3-FR-2004.00156

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Kuss, Damien; Laurain, Vincent; Garnier, Hugues; Vazquez, Jose; Zug, Mathieu

Abstract: This paper presents a data-based mechanistic modelling (DBM) approach to rainfall-runoff modelling based on the direct identification and estimation of continuous-time models from discrete-time series. It is argued that many mechanistic model parameters are more naturally defined in the context of continuous-time, differential equation models. As a results, there are advantages if such models are identified directly in this continuous-time form rather than being formulated and identified as discrete-time models. An illustrative example based on the analysis of rainfall-flow data from the Kerinou catchment in Brest demonstrates the relevance of the proposed modelling approach.

Keywords: Continuous Time System Estimation; Grey Box Modelling; Other

Identifier: 10.3182/20090706-3-FR-2004.00296

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: van Heusden, Klaske; Karimi, Alireza; Bonvin, Dominique

Abstract: This paper proposes a data-driven test for closed-loop stability. The test is based on a necessary and sufficient stability condition that can be verified without having to actually implement the controller. It uses a set of measurements from the plant but does not rely on a plant model. For infinite data length, a validated controller is guaranteed to stabilize the plant. In practice only a finite number of data can be used, and thus only an estimate of the stability condition can be obtained. A reliable stability test needs to take this estimation error into account, which introduces conservatism. The proposed test provides an intuitive trade-off between reliability and conservatism. A simulation example shows the effectiveness of the stability test.

Keywords: Identification for Control; Model Validation; Nonparametric Methods

Identifier: 10.3182/20090706-3-FR-2004.00174

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Barenthin Syberg, Märta; Wahlberg, Bo; Hjalmarsson, Håkan; Barkhagen, Mathias

Abstract: In this paper we present and discuss some data-driven methods for estimation of the L₂-gain of dynamical systems. Partial results on convergence and statistical properties are provided. The methods are based on multiple experiments on the system. The main idea is to directly estimate the maximizing input signal by using iterative experiments on the true system. We study such a data-driven method based on a stochastic gradient method. We show that this method is very closely related to the so-called power iteration method based on the power method in numerical analysis. Furthermore, it is shown that this method is applicable for linear systems with noisy measurements. We will also study L₂-gain estimation of Hammerstein systems. The stochastic gradient method and the power iteration method are evaluated and compared in simulation examples.

Keywords: Input and Excitation Design; Identification for Control; Model Validation

Identifier: 10.3182/20090706-3-FR-2004.00265

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Young, Peter; Pianosi, Francesca; Castelletti, Andrea; Soncini-Sessa, Rodolfo

Abstract: Abstract: An inflow prediction model is developed to compute flow from temperature records, taking into consideration snow-melt contribution to the flow using a Data-Based Mechanistic (DBM) modeling approach. DBM is used in order to keep at a minimum all the a-priori assumptions on the physical mechanism driving the flow formation process and to provide an a-posteriori meaningful interpretation of the model structure. A simulation version of the model is also identified based on such interpretation. The two models have been applied on the Jokulsa river basin, Iceland.

Keywords: Other; Nonlinear System Identification; Nonparametric Methods

Identifier: 10.3182/20090706-3-FR-2004.00292

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Oaki, Junji; Adachi, Shuichi

Abstract: The objective of our study is to build a precise model by applying the technique of system identification for the model-based control of a nonlinear robot arm, taking joint-elasticity into consideration. This paper proposes a systematic identification method, called "decoupling identification", for a serial two-link robot arm with elastic joints caused by the Harmonic drive reduction gears. The proposed method serves as an extension of the conventional rigid-joint-model-based identification. The robot arm is treated as a serial two-link two-inertia system with nonlinearity. The decoupling identification method using link-accelerometer signals enables the serial two-link two-inertia system to be divided into two linear one-link two-inertia systems. The MATLAB's commands for state-space model estimation are utilized in the proposed method. Physical parameters such as motor inertias, link inertias, joint-friction coefficients and joint-spring coefficients are estimated through the identified one-link two-inertia systems. Experimental results using a SCARA-type planar two-link robot arm with elastic reduction gears showed an accuracy of the proposed identification method.

Keywords: Multivariable System Identification; Nonlinear System Identification; Vibration and Model Analysis

Identifier: 10.3182/20090706-3-FR-2004.00236

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Van Derlinden, Eva; Van Impe, Jan F.M.

Abstract: Previous work showed that the exponential growth phase of E. coli K12 MG1655, grown in Brain Heart Infusion broth at temperatures close to its maximum temperature for growth is disturbed. Based on plate count data, microscopic images and literature, the existence of a heat resistant subpopulation was hypothesized (Van Derlinden et al. 2008). This hypothesis is here mathematically validated by means of a heterogeneous modeling approach. Two subpopulations are considered: one sensitive population and one population with increased heat tolerance. A large fraction of the initial population inactivates while the remaining smaller fraction is able to resist (or adapt to) the inimical temperature and grows. A heterogeneous model that encloses growth (resistant population) and inactivation (sensitive population) is used to describe the global population dynamics. Most experimental data can be predicted when taking into account experimental and parameter uncertainty via Monte Carlo simulation. The heterogeneous modeling approach yields accurate description of disturbed growth curves at super optimal temperatures, except for high initial cell densities. This study confirms the existence of a (small) heat resistant subpopulation in typical inoculum cultures of Escherichia coli K12 MG1655.

Keywords: Biological Systems; Nonlinear System Identification; Model Validation

Identifier: 10.3182/20090706-3-FR-2004.00208

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Yamamoto, Toru; Kawada, Kazuo; Kugemoto, Hidekazu; Kutsuwa, Yoshinori

Abstract: In the challenge to manufacture high quality products it is necessary to regularly monitor performance of control loops that regulate the quality variables of interest. This paper describes a unified approach of the control performance and the PID controller design which are based on the above control strategy. According to the proposed approach, the control performance is first monitored regularly. If the performance exceeds a user-defined threshold, the system identification is initiated and PID parameters are subsequently updated for the new model. Optimal PID parameters are calculated based on the LQG trade-off curve obtained for the re-identified process model. The behavior of the proposed scheme is evaluated by applying for real chemical processes.

Keywords: Identification for Control; Process Control; Closed Loop Identification

Identifier: 10.3182/20090706-3-FR-2004.00121

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: de Callafon, Raymond; Meyer, Jordan; Delson, Nathan

Abstract: This paper summarizes the mechanical and control design concepts of an inverted or unstable pendulum where stabilization is achieved by a moment exchange generated by a controlled symmetric rotation of a rotational inertia attached to the pendulum. The proposed design of the pendulum has a fixed bottom rotation or point of support as opposed to the usual vertically or horizontally moving point of support to stabilize the pendulum, allowing for small form factor desktop design of an inverted pendulum experiment. The symmetry of the rotational inertia allows for stabilization of the pendulum without the need to control the position of the mass attached to the pendulum. The paper reviews the design considerations, dynamic modeling, system identification and control design strategy to stabilize the pendulum.

Keywords: Mechanical and Aerospace; Grey Box Modelling; Identification for Control

Identifier: 10.3182/20090706-3-FR-2004.00076

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009

Authors: Ljung, Lennart; Singh, Rajiv; Zhang, Qinghua; Lindskog, Peter; Juditsky, Anatoly

Abstract: The paper describes additions to the MathWorks System Identification Toolbox, that handle the estimation of nonlinear models. Both structured grey-box models and general, flexible black-box models are covered. The idea is that the look and feel of the syntax, and the graphical user interface (GUI) should be as close as possible to the linear case. This presentation is focused on the GUI functionality and the possibilities to simulate the estimated models in Simulink.

Keywords:

Identifier: 10.3182/20090706-3-FR-2004.00086

Conference: 15th IFAC Symposium on System Identification, 2009

Location: Saint-Malo Convention Center, Saint-Malo, France

Start Date: Mon Jul 06 2009 - End Date: Wed Jul 08 2009