S. Thil and M. Gilson, A BAYESIAN APPROACH TO CLOSED-LOOP SYSTEM IDENTIFICATION, 16th IFAC World Congress, 2005.
DOI : 10.3182/20050703-6-CZ-1902.00108

URL : https://hal.archives-ouvertes.fr/hal-00152697

S. Thil, M. Gilson, and H. Garnier, Méthodes de compensation de biais pour l'identification de modèles erreurs en les variables, Journées Doctorales du GDR MACS, 2005.

S. Thil, H. Garnier, and M. Gilson, Une méthode pour l'identification de modèles modèlesà temps continu dans un contexte erreurs en les variables. Conférence Internationale Francophone d'Automatique (CIFA'2006), 2006.

S. Thil, H. Garnier, and M. Gilson, A cumulant statistics-based method for continuous-time errors-in-variables model identification. EURASIP Workshop on Total Least Squares and Errors-in-Variables Modeling, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00121389

H. Garnier, P. C. Young, S. Thil, and M. Gilson, Data-based Continuous-time Modelling of Environmental Systems. STIC & Environnement, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00167182

S. Thil, H. Garnier, M. Gilson, and K. Mahata, Continuous-time model identification from noisy input/output measurements using fourth-order cumulants. 46th Conference on Decision and Control (CDC'2007), 2007.
URL : https://hal.archives-ouvertes.fr/hal-00167446

S. Thil, H. Garnier, and M. Gilson, Third-order cumulants based methods for continuous-time errors-in-variables model identification, Automatica, vol.44, issue.3, 2008.
DOI : 10.1016/j.automatica.2007.07.010

URL : https://hal.archives-ouvertes.fr/hal-00152696

S. Thil, M. Gilson, H. D. Garnier-]-b, M. Anderson, and . Deistler, On instrumental variable-based methods for errors-in-variables model identification. 17th IFAC World Congress Identifiability in dynamic errors-invariables models, Journal of Time Series Analysis, vol.5, issue.1, pp.1-13, 1984.
URL : https://hal.archives-ouvertes.fr/hal-00261403

M. [. Anderson and . Deistler, Dynamic errors-in-variables systems with three variables, Automatica, vol.23, issue.5, pp.611-616, 1987.
DOI : 10.1016/0005-1098(87)90056-2

]. R. Adc77 and . Adcock, Note on the methods of least squares. The Analyst, pp.183-184, 1877.

]. R. Adc78 and . Adcock, A problem in least squares. The Analyst, pp.53-54, 1878.

W. [. Anderson and . Edmondson, System identification with noisy input-output data using a cumulant-based Steiglitz-McBride algorithm, IEEE Transactions on Circuits and Systems II : Analog and Digital Signal Processing, pp.1021-1024, 1997.
DOI : 10.1109/82.580855

G. [. Anderson and . Giannakis, Noisy input/output system identification using cumulants and the Steiglitz-McBride algorithm, IEEE Transactions on Signal Processing, vol.44, issue.4, pp.1021-1024, 1996.
DOI : 10.1109/78.492561

G. [. Agüero and . Goodwin, Identifiability of errors-in-variables dynamic systems, 14th IFAC Symposium on System Identification, pp.196-201, 2006.

G. [. Agüero and . Goodwin, Identifiability of errors-in-variables dynamic systems, Automatica, vol.44, issue.2, 2008.

P. [. Aström, E. J. Hagander, and . Sternby, Zeros of sampled systems, 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, pp.31-38, 1984.
DOI : 10.1109/CDC.1980.271968

]. H. Aka66 and . Akaike, On the use of non-Gaussian process in the identification of a linear dynamic system, Annals of the Institute of Statistical Mathematics, vol.18, issue.3, pp.269-276, 1966.

J. [. Anderson and . Moore, Optimal Filtering, IEEE Transactions on Systems, Man, and Cybernetics, vol.12, issue.2, 1979.
DOI : 10.1109/TSMC.1982.4308806

]. B. And85 and . Anderson, Identification of scalar errors-in-variables models with dynamics, Automatica, vol.21, pp.709-716, 1985.

P. [. Beghelli, E. U. Castaldi, and . Soverini, A frequential approach for errorsin-variables models, European Control Conference (ECC'97), 1997.

R. [. Beghelli, E. U. Guidorzi, and . Soverini, The frisch scheme in dynamic system identification, Automatica, vol.26, issue.1, pp.171-176, 1990.
DOI : 10.1016/0005-1098(90)90168-H

B. [. Bondon and . Picinbono, De la blancheur et de ses transformations, pp.385-395, 1990.

]. D. Bri81 and . Brillinger, Time Series, Data Analysis and Theory, 1981.

]. D. Bri01 and . Brillinger, Time Series, Data Analysis and Theory, Society for Industrial and Applied Mathematics, 2001.

[. Chen and B. Chen, A higher-order correlation method for model-order and parameter estimation, Automatica, vol.30, issue.8, pp.1339-1344, 1994.
DOI : 10.1016/0005-1098(94)90113-9

]. P. Cs96a, U. Castaldi, and . Soverini, Identification of dynamic errors-in-variables models, Automatica, vol.32, issue.4, pp.631-636, 1996.

M. Cerdevall and P. Stoica, System identification from noisy measurements by using instrumental variables and subspace fitting. Circuits, Systems and Signal Processing, pp.275-290, 1996.

M. [. Chou and . Verhaegen, Subspace Algorithms for the Identification of Multivariable Dynamic Errors-in-Variables Models**This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised form by Associate Editor H. Hjalmarsson under the direction of Editor Torsten S??derstr??m., Automatica, vol.33, issue.10, pp.1857-1869, 1997.
DOI : 10.1016/S0005-1098(97)00092-7

M. [. Chou, E. R. Verhaegen, and . Johansson, Continuous-time identification of SISO systems using Laguerre functions, IEEE Transactions on Signal Processing, vol.47, issue.2, pp.349-362, 1999.
DOI : 10.1109/78.740121

M. Deistler and B. D. Anderson, Linear dynamic errors in variables models: Some structure theory, Journal of Econometrics, vol.41, pp.39-63, 1989.
DOI : 10.1007/BFb0042272

]. M. Dei86, S. Deistler, and . Bittanti, Linear errors-in-variables models, Time Series and Linear Systems Lecture Notes in Control and Information Sciences, vol.86, pp.39-63, 1986.

G. [. Delopoulos and . Giannakis, Consistent identification of stochastic linear systems with noisy input-output data, Automatica, vol.30, issue.8, pp.1271-1294, 1994.
DOI : 10.1016/0005-1098(94)90108-2

R. [. Diversi, E. U. Guidorzi, and . Soverini, A new criterion in EIV identification and filtering applications, 13th IFAC Symposium on System Identification, pp.1993-1998, 2003.

]. M. De-mathelin, Panorama des algorithmes récursifs d'estimation paramétrique, Identification et Commande Adaptative, pp.101-160, 2001.

M. [. Ekman, E. T. Hong, and . Söderström, A SEPARABLE NONLINEAR LEAST-SQUARES APPROACH FOR IDENTIFICATION OF LINEAR SYSTEMS WITH ERRORS IN VARIABLES, 14th IFAC Symposium on System Identification, 2006.
DOI : 10.3182/20060329-3-AU-2901.00022

]. M. Ekm05 and . Ekman, Identification of linear systems with errors in variables using separable nonlinear least squares, 16th IFAC World Congress on Automatic Control, pp.178-183, 2005.

U. Forssell, F. Gustafsson, and E. T. Mckelvey, Time-domain identification of dynamic errors-in-variables systems using periodic excitation signals, 14th IFAC World Congress on Automatic Control, 1999.

H. [. Fernando and . Nicholson, Identification of linear systems with input and output noise: the Koopmans-Levin method, IEE Proceedings D Control Theory and Applications, vol.132, issue.1, pp.30-36, 1985.
DOI : 10.1049/ip-d.1985.0007

B. [. Friedlander and . Porat, Asymptotically optimal estimation of MA and ARMA parameters of non-Gaussian processes from high-order moments, IEEE Transactions on Automatic Control, vol.35, issue.1, pp.27-35, 1990.
DOI : 10.1109/9.45140

]. R. Fri34 and . Frisch, Statistical confluence analysis by means of complete regression systems, Economics Institute, 1934.

]. R. Gea42 and . Geary, Inherent relations between random variables, Proceedings of the Royal Irish Academy, pp.63-76, 1942.

]. G. Gia90 and . Giannakis, On the identifiability of non-gaussian ARMA models using cumulants, IEEE Transactions on Automatic Control, vol.35, issue.1, pp.18-26, 1990.

C. [. Golub and . Van-loan, An Analysis of the Total Least Squares Problem, SIAM Journal on Numerical Analysis, vol.17, issue.6, pp.883-893, 1980.
DOI : 10.1137/0717073

J. [. Giannakis, Identification of nonminimum phase systems using higher order statistics, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.37, issue.3, pp.360-377, 1989.
DOI : 10.1109/29.21704

H. Garnier, M. Mensler, and E. A. Richard, Continuous-time model identification from sampled data: Implementation issues and performance evaluation, International Journal of Control, vol.76, issue.13, pp.1337-1357, 2003.
DOI : 10.1080/0020717031000149636

R. [. Guillaume, E. J. Pintelon, and . Schoukens, Robust parametric transfer function estimation using complex logarithmic frequency response data, IEEE Transactions on Automatic Control, vol.40, issue.7, pp.1180-1190, 1995.
DOI : 10.1109/9.400493

M. Gilson and P. Van-den-hof, On the relation between a bias-eliminated least-squares (BELS) and an IV estimator in closed-loop identification, Automatica, vol.37, issue.10, pp.1593-1600, 2001.
DOI : 10.1016/S0005-1098(01)00119-4

M. Gilson and P. Van-den-hof, Instrumental variable methods for closed-loop system identification, Automatica, vol.41, issue.2, pp.241-249, 2005.
DOI : 10.1016/j.automatica.2004.09.016

URL : https://hal.archives-ouvertes.fr/hal-00089684

P. [. Garnier, S. Young, E. M. Thil, and . Gilson, Data-based continuoustime modelling of environmental systems, Dans STIC & environnement, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00167182

H. [. Huselstein and . Garnier, An approach to continuous-time model identification from non-uniformly sampled data, Proceedings of the 41st IEEE Conference on Decision and Control, 2002., 2002.
DOI : 10.1109/CDC.2002.1184570

]. M. Hsz06a, T. Hong, W. X. Söderström, and . Zheng, Accuracy analysis of biaseliminating least squares for errors-in-variables identification, 14th IFAC Symposium on System Identification, pp.190-195, 2006.

M. Hong, T. Söderström, and W. X. Zheng, A simplified form of the biaseliminating least squares method for errors-in-variables identification, 2006.

T. [. Hong, W. X. Söderström, and . Zheng, Accuracy analysis of bias-eliminating least squares estimates for errors-in-variables systems, Automatica, vol.43, issue.9, pp.1590-1596, 2007.
DOI : 10.1016/j.automatica.2007.02.002

M. Ikenoue, S. Kanae, Z. Yand, and E. K. Wada, Identification of noisy inputoutput system using bias-compensated least squares method, 16th IFAC World Congress on Automatic Control, 2005.

Y. [. Inouye and . Suga, Identification of linear systems with noisy input using input-output cumulants, International Journal of Control, vol.59, issue.5, pp.1231-1253, 1994.
DOI : 10.1109/9.126580

H. [. Inouye and . Tsuchiya, Identification of linear systems using input-output cumulants, International Journal of Control, vol.3, issue.6, pp.1431-1448, 1991.
DOI : 10.1109/TAC.1970.1099514

[. Jia, M. Ikenoue, C. Jin, and E. K. Wada, On bias compensated least squares method for noisy input-output system identification, 40th IEEE Conference on Decision and Control, pp.3332-3337, 2001.

[. Jia, C. Jin, Z. Yang, and E. K. Wada, On the relationship between BELS and IV methods, 41st IEEE Conference on Decision and Control, pp.3440-3445, 2002.

]. R. Joh93 and . Johansson, System Modeling and Identification, 1993.

]. T. Koo36 and . Koopmans, Linear regression analysis of economic time series, Thèse de doctorat, 1936.

T. [. Karlsson, E. P. Söderström, and . Stoica, The Cram??r???Rao lower bound for noisy input???output systems, Signal Processing, vol.80, issue.11, pp.2421-2447, 2000.
DOI : 10.1016/S0165-1684(00)00126-2

]. M. Lev64 and . Levin, Estimation of a system pulse transfer function in the presence of noise, IEEE Transactions on Automatic Control, vol.9, issue.3, pp.229-235, 1964.

]. L. Lju87 and . Ljung, System Identification : Theory for the User, 1987.

]. L. Lju99 and . Ljung, System Identification : Theory for the User, 1999.

]. L. Lju03 and . Ljung, Initialisation aspects for subspace and output?error identification methods, European Control Conference (ECC'03), 2003.

]. K. Mah07 and . Mahata, An improved bias-compensation approach for errors-in-variables model identification, Automatica, vol.43, issue.8, pp.1339-1354, 2007.

D. [. Moussaoui, E. A. Brie, and . Richard, Regularization aspects in continuous-time model identification, Automatica, vol.41, issue.2, pp.197-208, 2005.
DOI : 10.1016/j.automatica.2004.10.008

URL : https://hal.archives-ouvertes.fr/hal-00456203

]. P. Mcc87 and . Mccullagh, Tensor Methods in Statistics. Monographs on Statistics and Applied Probability, 1987.

]. J. Men91, Tutorial on high-order statistics (spectra) in signal processing and system theory : theoretical results and some applications, Proceedings of the IEEE, pp.278-305, 1991.

]. M. Men99 and . Mensler, Analyse etétudeetétude comparative de méthodes d'identification des systèmessystèmesà représentation continue

H. [. Mahata and . Garnier, Direct identification of continuous-time errorsin-variables models, 16th IFAC World Congress on Automatic Control, 2005.

H. [. Mahata and . Garnier, Identification of continuous-time errors-in-variables models, Automatica, vol.49, issue.9, pp.1470-1490, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00119875

I. Markovsky, A. Kukush, and E. S. Van-huffel, ON ERRORS-IN-VARIABLES ESTIMATION WITH UNKNOWN NOISE VARIANCE RATIO, 14th IFAC Symposium on System Identification, 2006.
DOI : 10.3182/20060329-3-AU-2901.00021

R. [. Nikias and . Pan, Time delay estimation in unknown Gaussian spatially correlated noise, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.36, issue.11, pp.1706-1714, 1988.
DOI : 10.1109/29.9008

A. [. Nikias and . Petropulu, Higher-order Spectra Analysis. Signal Processing Series, 1993.

B. [. Porat and . Friedlander, Performance analysis of parameter estimation algorithms based on high-order moments, International Journal of Adaptive Control and Signal Processing, vol.10, issue.3, pp.191-229, 1989.
DOI : 10.1002/acs.4480030302

J. [. Pintelon and . Schoukens, System Identification : A Frequency Domain Approach, 2001.

E. [. Pronzato and . Walter, Eliminating Suboptimal Local Minimizers in Nonlinear Parameter Estimation, Technometrics, vol.43, issue.4, pp.434-442, 2001.
DOI : 10.1198/00401700152672528

E. [. Pronzato and . Walter, CommentéviterCommentéviter les optimiseurs locaux parasites en estimation paramétrique non linéaire, Journal Européen des Systèmes Automatisés, vol.36, issue.3, pp.351-362, 2002.

]. O. Rei41, Confluence analysis by means of lag moments and other methods of confluence analysis, Econometrica, vol.9, issue.1, pp.1-24, 1941.

]. O. Rei50, Identifiability of a linear relation between variables that are subject to error, Econometrica, vol.18, issue.4, pp.375-389, 1950.

M. Rosenblatt and J. W. Van-ness, Estimation of the Bispectrum, The Annals of Mathematical Statistics, vol.36, issue.4, pp.420-436, 1965.
DOI : 10.1214/aoms/1177699987

M. [. Stoica, E. A. Cerdevall, and . Eriksson, Combined instrumental variable and subspace fitting approach to parameter estimation of noisy input-output systems, IEEE Transactions on Signal Processing, vol.43, issue.10, pp.432386-2397, 1995.
DOI : 10.1109/78.469852

M. [. Scherrer and . Deistler, A Structure Theory for Linear Dynamic Errors-in-Variables Models, SIAM Journal on Control and Optimization, vol.36, issue.6, pp.2148-2175, 1998.
DOI : 10.1137/S0363012994262464

M. [. Söderström and . Hong, IDENTIFICATION OF DYNAMIC ERRORS-IN-VARIABLES SYSTEMS WITH PERIODIC DATA, 16th IFAC World Congress on Automatic Control, 2005.
DOI : 10.3182/20050703-6-CZ-1902.00136

M. [. Söderström, W. X. Hong, and . Zheng, Convergence properties of bias-eliminating algorithms for errors-in-variables identification, International Journal of Adaptive Control and Signal Processing, vol.13, issue.9, pp.703-722, 2005.
DOI : 10.1002/acs.879

E. [. Söderström, K. Larsson, E. M. Mahata, and . Mossberg, USING CONTINUOUS-TIME MODELING FOR ERRORS-IN-VARIABLES IDENTIFICATION, 14th IFAC Symposium on System Identification, pp.428-433, 2006.
DOI : 10.3182/20060329-3-AU-2901.00064

L. [. Steiglitz and . Mcbride, A technique for the identification of linear systems, IEEE Transactions on Automatic Control, vol.10, issue.4, pp.2386-2397, 1965.
DOI : 10.1109/TAC.1965.1098181

K. [. Söderström and . Mahata, On instrumental variable and total least squares approaches for identification of noisy systems, International Journal of Control, vol.75, issue.6, pp.381-389, 2002.
DOI : 10.1016/0005-1098(70)90098-1

K. [. Söderström, E. U. Mahata, and . Soverini, Identification of dynamic errors-in-variables models: Approaches based on two-dimensional ARMA modeling of the data, Automatica, vol.39, issue.5, pp.929-935, 2003.
DOI : 10.1016/S0005-1098(03)00033-5

A. [. Stoica and . Nehorai, On the uniqueness of prediction error models for systems with noisy input-output data, Automatica, vol.23, issue.4, pp.541-543, 1987.
DOI : 10.1016/0005-1098(87)90083-5

J. [. Stuart and . Ord, Kendall's Advanced Theory of Statistics

]. T. Söd81 and . Söderström, Identification of stochastic linear systems in presence of input noise, Automatica, vol.17, issue.5, pp.713-725, 1981.

]. T. Söd02 and . Söderström, Discrete-time Stochastic Systems ? Estimation and Control

]. T. Söd03 and . Söderström, Why are errors-in-variables problems often tricky ? Dans European Control Conference (ECC'03), 2003.

]. T. Söd05 and . Söderström, Accuracy analysis of the Frisch estimates for identifying errors-in-variables systems, 44th IEEE Conference on Decision and Control, 2005.

]. T. Söd06 and . Söderström, On computing the Cramer-Rao bound and covariance matrices for PEM estimates in linear state space models, 14th IFAC Symposium on System Identification, pp.600-605, 2006.

]. T. Söd07 and . Söderström, Errors-in-variables methods in system identification, Automatica, vol.43, issue.6, pp.939-958, 2007.

]. T. Söd08 and . Söderström, Extending the Frisch scheme for errors-in-variables identification to correlated output noise, International Journal of Adaptive Control and Signal Processing, vol.22, issue.1, pp.55-73, 2008.

]. V. Sol86 and . Solo, Identifiability of time series models with errors in variables, Journal of Applied Probability, vol.23, pp.63-71, 1986.

R. [. Schoukens and . Pintelon, Identification of Linear Systems : a Practical Guideline to Accurate Modeling, 1991.

J. Schoukens, R. Pintelon, G. Vandersteen, and E. P. Guillaume, Frequencydomain system identification using non-parametric noise models estimated from a small number of data sets, Automatica, vol.30, issue.6, pp.1073-1086, 1997.

]. T. Ss81a, P. Söderström, and . Stoica, Comparison of some instrumental variable methods ? consistency and accuracy aspects, Automatica, vol.17, issue.1, pp.101-115, 1981.

]. P. Ss81b, T. Stoica, and . Söderström, The Steiglitz-McBride identification algorithm revisited ? convergence analysis and accuracy aspects, IEEE Transactions on Automatic Control, vol.26, issue.3, pp.712-717, 1981.

P. [. Söderström and . Stoica, Instrumental Variable Methods for System Identification, 1983.
DOI : 10.1007/BFb0009019

P. [. Söderström and . Stoica, System identification. Series in Systems and Control Engineering, 1989.

Z. [. Sagara, E. K. Yang, and . Wada, Identification of continuous systems from noisy sampled input-output data, IFAC Symposium on System Identification and Parameter Estimation, pp.1922-1627, 1991.

W. [. Söderström, E. P. Zheng, and . Stoica, Comments on "On a least-squares-based algorithm for identification of stochastic linear systems", IEEE Transactions on Signal Processing, vol.47, issue.5, pp.1395-1396, 1999.
DOI : 10.1109/78.757229

M. [. Thil, E. H. Gilson, and . Garnier, Méthodes de compensation de biais pour l'identification de modèles erreurs en les variables, 2005.

]. S. Tgg08a, H. Thil, E. M. Garnier, and . Gilson, Third?order cumulants based methods for continuous?time errors?in?variables model identification, Automatica, vol.44, issue.3, pp.647-658, 2008.

]. S. Tgg08b, M. Thil, E. H. Gilson, and . Garnier, On instrumental variable methods for errors-in-variables model identification, 17th IFAC World Congress, 2008.

H. [. Thil, M. Garnier, E. K. Gilson, and . Mahata, Continuous-time model identification from noisy input/output measurements using fourth-order cumulants, 2007 46th IEEE Conference on Decision and Control, 2007.
DOI : 10.1109/CDC.2007.4434318

URL : https://hal.archives-ouvertes.fr/hal-00167446

]. M. Tho05 and . Thomassin, Estimation de retard dans des conditions expérimentales passives . ApplicationàApplicationà l'identification d'un bief derivì ere, Thèse de doctorat, 2005.

]. S. Ths-+-08, M. Thil, T. Hong, M. Södertström, E. H. Gilson et al., Statistical analysis of a third-order cumulants based algorithm for discrete-time errors-invariables identification, 17th IFAC World Congress, 2008.

E. [. Tugnait and . Liu, Model validation and order selection for linear model fitting using thirdand fourth-order cumulants, IEEE Transactions on Signal Processing, vol.47, issue.9, pp.2433-2443, 1999.
DOI : 10.1109/78.782187

]. J. Tug91 and . Tugnait, On time delay estimation with unknown spatially correlated Gaussian noise using fourth order cumulants and cross cumulants, IEEE Transactions on Signal Processing, pp.1258-1267, 1991.

]. J. Tug92 and . Tugnait, Stochastic system identification with noisy input using cumulant statistics, IEEE Transactions on Automatic Control, vol.37, issue.4, pp.476-485, 1992.

]. J. Tug94 and . Tugnait, Linear model validation and order selection using higher order statistics, IEEE Transactions on Signal Processing, vol.42, issue.7, pp.1728-1736, 1994.

]. J. Tug95 and . Tugnait, An improved test for linear model validation and order selection using higher order statistics, IEEE Signal Processing Letters, vol.2, issue.6, pp.123-125, 1995.

Y. [. Tugnait and . Ye, Stochastic system identification with noisy input-output measurements using polyspectra, IEEE Transactions on Automatic Control, vol.40, issue.4, pp.670-683, 1995.
DOI : 10.1109/9.376110

G. [. Unbehauen and . Rao, Continuous-time approaches to system identification???A survey, Automatica, vol.26, issue.1, pp.23-35, 1990.
DOI : 10.1016/0005-1098(90)90155-B

]. P. Vdh96 and . Van-den-hof, System identification, 1996.

. S. Vh02 and . Van-huffel, Total least squares and errors-in-variables modeling, 2002.

K. Wong, D. Feng, and W. Siu, Generalized linear least squares algorithm for non-uniformly sampled biomedical system identification with possible repeated eigenvalues. Computer methods and programs in biomedicine, pp.167-177, 1998.

L. [. Walter and . Pronzato, Identification de Modèles ParamétriquesParamétriques`Paramétriquesà partir de Données Expérimentales, 1994.

P. C. Young, H. Garnier, and E. M. Gilson, Refined Instrumental Variable Identification of Continuous-time Hybrid Box-Jenkins Models, Garnier et L. Wang, ´ editeurs, Identification of Continuous-time Models from Sampled Data, 2008.
DOI : 10.1007/978-1-84800-161-9_4

URL : https://hal.archives-ouvertes.fr/hal-00161983

A. [. Young and . Jakeman, Refined instrumental variable methods of recursive time-series analysis Part III. Extensions, International Journal of Control, vol.29, issue.4, pp.741-764, 1980.
DOI : 10.1080/00207178008961080

]. P. You64 and . Young, In flight dynamic checkout -a discussion, IEEE Transactions on Aerospace, vol.2, pp.1106-1111, 1964.

]. P. You70 and . Young, An instrumental variable method for real-time identification of a noisy process, Automatica, vol.6, issue.2, pp.271-287, 1970.

]. P. You81 and . Young, Parameter estimation for continuous?time models -a survey, Automatica, vol.17, issue.1, pp.23-39, 1981.

]. P. You84 and . Young, Recursive Estimation and Time Series Analysis, 1984.

Z. Yang, S. Sagara, and E. K. Wada, Identification of continuous-time systems from sampled input-output data using bias eliminating techniques, Control Theory and Advanced Technology, vol.9, issue.1, pp.53-75, 1993.

]. W. Zhe98 and . Zheng, Transfer function estimation from noisy input and output data, International Journal of Adaptive Control and Signal Processing, vol.12, issue.4, pp.365-380, 1998.

]. W. Zhe99 and . Zheng, On least-squares identification of stochastic linear systems with noisy input-output data, International Journal of Adaptive Control and Signal Processing, vol.13, issue.3, pp.131-143, 1999.

]. W. Zhe00 and . Zheng, Unbiased identification of stochastic linear systems from noisy input and output measurements, 39th IEEE Conference on Decision and Control, pp.2710-2715, 2000.

]. W. Zhe02 and . Zheng, A bias correction method for identification of linear dynamic errors-in-variables models, IEEE Transactions on Automatic Control, vol.47, issue.7, pp.1142-1147, 2002.