The Experts below are selected from a list of 105 Experts worldwide ranked by ideXlab platform
C. Goutte - One of the best experts on this subject based on the ideXlab platform.
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lag space estimation in time series modelling
International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: C. GoutteAbstract:The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the Regressor Vector a.k.a. the input layer in a neural network. We give a rough description of the problem, insist on the concept of generalisation, and propose a generalisation-based method. We compare it to a non-parametric test, and carry out experiments, both on the well-known Henon map, and on a real data set.
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ICASSP - Lag space estimation in time series modelling
1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: C. GoutteAbstract:The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the Regressor Vector a.k.a. the input layer in a neural network. We give a rough description of the problem, insist on the concept of generalisation, and propose a generalisation-based method. We compare it to a non-parametric test, and carry out experiments, both on the well-known Henon map, and on a real data set.
Stéphane Lecoeuche - One of the best experts on this subject based on the ideXlab platform.
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a recursive identification algorithm for switched linear affine models
Nonlinear Analysis: Hybrid Systems, 2011Co-Authors: Laurent Bako, Eric Duviella, Khaled Boukharouba, Stéphane LecoeucheAbstract:Abstract In this work, a recursive procedure is derived for the identification of switched linear models from input–output data. Starting from some initial values of the parameter Vectors that represent the different submodels, the proposed algorithm alternates between data assignment to submodels and parameter update. At each time instant, the discrete state is determined as the index of the submodel that, in terms of the prediction error (or the posterior error), appears to have most likely generated the Regressor Vector observed at that instant. Given the estimated discrete state, the associated parameter Vector is updated based on recursive least squares or any fast adaptive linear identifier. Convergence of the whole procedure although not theoretically proved, seems to be easily achieved when enough rich data are available. It has been also observed that by appropriately choosing the data assignment criterion, the proposed on-line method can be extended to deal also with the identification of piecewise affine models. Finally, performance is tested through some computer simulations and the modeling of an open channel system.
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A recursive identification algorithm for switched linear/affine models
Nonlinear Analysis: Hybrid Systems, 2011Co-Authors: Laurent Bako, Eric Duviella, Khaled Boukharouba, Stéphane LecoeucheAbstract:Abstract In this work, a recursive procedure is derived for the identification of switched linear models from input–output data. Starting from some initial values of the parameter Vectors that represent the different submodels, the proposed algorithm alternates between data assignment to submodels and parameter update. At each time instant, the discrete state is determined as the index of the submodel that, in terms of the prediction error (or the posterior error), appears to have most likely generated the Regressor Vector observed at that instant. Given the estimated discrete state, the associated parameter Vector is updated based on recursive least squares or any fast adaptive linear identifier. Convergence of the whole procedure although not theoretically proved, seems to be easily achieved when enough rich data are available. It has been also observed that by appropriately choosing the data assignment criterion, the proposed on-line method can be extended to deal also with the identification of piecewise affine models. Finally, performance is tested through some computer simulations and the modeling of an open channel system.
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Switched affine models for describing nonlinear systems
2009Co-Authors: Laurent Bako, Boukharouba Khaled, Eric Duviella, Stéphane LecoeucheAbstract:In this work, a recursive procedure is derived for the identification of switched affine models from input-output data. Starting from some initial values of the parameter Vectors that represent the different submodels, the proposed algorithm alternates between data assignment to submodels and parameter update. At each time instant, the discrete state is determined as the index of the submodel that, in term of the prediction error, appears to have most likely generated the Regressor Vector observed at that instant. Given the estimated discrete state, the associated parameter Vector is updated based on recursive least squares. Convergence of the whole procedure although not theoretically proved, seems to be easily achieved when enough rich data are available. Finally performance is tested through some computer simulations and the modeling of an open channel system.
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ADHS - Switched affine models for describing nonlinear systems
IFAC Proceedings Volumes, 2009Co-Authors: Laurent Bako, Eric Duviella, Khaled Boukharouba, Stéphane LecoeucheAbstract:Abstract In this work, a recursive procedure is derived for the identification of switched affine models from input-output data. Starting from some initial values of the parameter Vectors that represent the different submodels, the proposed algorithm alternates between data assignment to submodels and parameter update. At each time instant, the discrete state is determined as the index of the submodel that, in term of the prediction error, appears to have most likely generated the Regressor Vector observed at that instant. Given the estimated discrete state, the associated parameter Vector is updated based on recursive least squares. Convergence of the whole procedure although not theoretically proved, seems to be easily achieved when enough rich data are available. Finally performance is tested through some computer simulations and the modeling of an open channel system.
J. Mills - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - A stability problem in sign-sign adaptive algorithms
ICASSP '86. IEEE International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: C. Rohrs, C Johnson, J. MillsAbstract:It has been shown by example that the often used sign-sign variant of the LMS can be unstable and produce unbounded parameter estimates. In this paper a modified sufficient excitation condition on the Regressor Vector for the sign-sign variant is introduced. Satisfaction of the excitation condition guarantees that there exists a sufficiently small step size to produce parameter estimates which converge exponentially to within a region of the correct parameters. The counter example to stability of sign-sign algorithms is reexamined in light of the new excitation condition and it is shown that the excitation condition correctly predicts when instability will occur in this example.
Laurent Bako - One of the best experts on this subject based on the ideXlab platform.
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a recursive identification algorithm for switched linear affine models
Nonlinear Analysis: Hybrid Systems, 2011Co-Authors: Laurent Bako, Eric Duviella, Khaled Boukharouba, Stéphane LecoeucheAbstract:Abstract In this work, a recursive procedure is derived for the identification of switched linear models from input–output data. Starting from some initial values of the parameter Vectors that represent the different submodels, the proposed algorithm alternates between data assignment to submodels and parameter update. At each time instant, the discrete state is determined as the index of the submodel that, in terms of the prediction error (or the posterior error), appears to have most likely generated the Regressor Vector observed at that instant. Given the estimated discrete state, the associated parameter Vector is updated based on recursive least squares or any fast adaptive linear identifier. Convergence of the whole procedure although not theoretically proved, seems to be easily achieved when enough rich data are available. It has been also observed that by appropriately choosing the data assignment criterion, the proposed on-line method can be extended to deal also with the identification of piecewise affine models. Finally, performance is tested through some computer simulations and the modeling of an open channel system.
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A recursive identification algorithm for switched linear/affine models
Nonlinear Analysis: Hybrid Systems, 2011Co-Authors: Laurent Bako, Eric Duviella, Khaled Boukharouba, Stéphane LecoeucheAbstract:Abstract In this work, a recursive procedure is derived for the identification of switched linear models from input–output data. Starting from some initial values of the parameter Vectors that represent the different submodels, the proposed algorithm alternates between data assignment to submodels and parameter update. At each time instant, the discrete state is determined as the index of the submodel that, in terms of the prediction error (or the posterior error), appears to have most likely generated the Regressor Vector observed at that instant. Given the estimated discrete state, the associated parameter Vector is updated based on recursive least squares or any fast adaptive linear identifier. Convergence of the whole procedure although not theoretically proved, seems to be easily achieved when enough rich data are available. It has been also observed that by appropriately choosing the data assignment criterion, the proposed on-line method can be extended to deal also with the identification of piecewise affine models. Finally, performance is tested through some computer simulations and the modeling of an open channel system.
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Switched affine models for describing nonlinear systems
2009Co-Authors: Laurent Bako, Boukharouba Khaled, Eric Duviella, Stéphane LecoeucheAbstract:In this work, a recursive procedure is derived for the identification of switched affine models from input-output data. Starting from some initial values of the parameter Vectors that represent the different submodels, the proposed algorithm alternates between data assignment to submodels and parameter update. At each time instant, the discrete state is determined as the index of the submodel that, in term of the prediction error, appears to have most likely generated the Regressor Vector observed at that instant. Given the estimated discrete state, the associated parameter Vector is updated based on recursive least squares. Convergence of the whole procedure although not theoretically proved, seems to be easily achieved when enough rich data are available. Finally performance is tested through some computer simulations and the modeling of an open channel system.
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ADHS - Switched affine models for describing nonlinear systems
IFAC Proceedings Volumes, 2009Co-Authors: Laurent Bako, Eric Duviella, Khaled Boukharouba, Stéphane LecoeucheAbstract:Abstract In this work, a recursive procedure is derived for the identification of switched affine models from input-output data. Starting from some initial values of the parameter Vectors that represent the different submodels, the proposed algorithm alternates between data assignment to submodels and parameter update. At each time instant, the discrete state is determined as the index of the submodel that, in term of the prediction error, appears to have most likely generated the Regressor Vector observed at that instant. Given the estimated discrete state, the associated parameter Vector is updated based on recursive least squares. Convergence of the whole procedure although not theoretically proved, seems to be easily achieved when enough rich data are available. Finally performance is tested through some computer simulations and the modeling of an open channel system.
C. Rohrs - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - A stability problem in sign-sign adaptive algorithms
ICASSP '86. IEEE International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: C. Rohrs, C Johnson, J. MillsAbstract:It has been shown by example that the often used sign-sign variant of the LMS can be unstable and produce unbounded parameter estimates. In this paper a modified sufficient excitation condition on the Regressor Vector for the sign-sign variant is introduced. Satisfaction of the excitation condition guarantees that there exists a sufficiently small step size to produce parameter estimates which converge exponentially to within a region of the correct parameters. The counter example to stability of sign-sign algorithms is reexamined in light of the new excitation condition and it is shown that the excitation condition correctly predicts when instability will occur in this example.
