The Experts below are selected from a list of 36 Experts worldwide ranked by ideXlab platform
Patrick Guillaume - One of the best experts on this subject based on the ideXlab platform.
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Brief paper: Box-Jenkins identification revisited-Part III
Automatica, 2007Co-Authors: Rik Pintelon, Joannes Schoukens, Patrick GuillaumeAbstract:Part I of this series of three papers handles the identification of single input single Output Box-Jenkins models on arbitrary frequency grids in an open and closed loop setting. Part II discusses the computational aspects and illustrates the theory on simulations and a real life problem. This paper extends the results of Parts I and II to multiple input multiple Output systems. Contrary to the classical time domain approach, the presented technique does not require symbolic calculus for multiple Output polynomial Box-Jenkins models.
Rik Pintelon - One of the best experts on this subject based on the ideXlab platform.
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Brief paper: Box-Jenkins identification revisited-Part III
Automatica, 2007Co-Authors: Rik Pintelon, Joannes Schoukens, Patrick GuillaumeAbstract:Part I of this series of three papers handles the identification of single input single Output Box-Jenkins models on arbitrary frequency grids in an open and closed loop setting. Part II discusses the computational aspects and illustrates the theory on simulations and a real life problem. This paper extends the results of Parts I and II to multiple input multiple Output systems. Contrary to the classical time domain approach, the presented technique does not require symbolic calculus for multiple Output polynomial Box-Jenkins models.
Joannes Schoukens - One of the best experts on this subject based on the ideXlab platform.
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Brief paper: Box-Jenkins identification revisited-Part III
Automatica, 2007Co-Authors: Rik Pintelon, Joannes Schoukens, Patrick GuillaumeAbstract:Part I of this series of three papers handles the identification of single input single Output Box-Jenkins models on arbitrary frequency grids in an open and closed loop setting. Part II discusses the computational aspects and illustrates the theory on simulations and a real life problem. This paper extends the results of Parts I and II to multiple input multiple Output systems. Contrary to the classical time domain approach, the presented technique does not require symbolic calculus for multiple Output polynomial Box-Jenkins models.
Håkan Hjalmarsson - One of the best experts on this subject based on the ideXlab platform.
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The Box-Jenkins Steiglitz-McBride algorithm
Automatica, 2016Co-Authors: Yucai Zhu, Håkan HjalmarssonAbstract:An algorithm for identification of single-input single-Output Box-Jenkins models is presented. It consists of four steps: firstly a high order ARX model is estimated; secondly, the input-Output data is filtered with the inverse of the estimated disturbance model; thirdly, the filtered data is used in the Steiglitz-McBride method to recover the system dynamics; in the final step, the noise model is recovered by estimating an ARMA model from the residuals of the third step. The relationship to other identification methods, in particular the refined instrumental-variable method, are elaborated upon. A Monte Carlo simulation study with an oscillatory system is presented and these results are complemented with an industrial case study. The algorithm can easily be generalized to multi-input single-Output models with common denominator.
A.f. Zuur - One of the best experts on this subject based on the ideXlab platform.
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An extension of Box-Jenkins transfer/noise models for spatial interpolation of groundwater head series
Journal of Hydrology, 1997Co-Authors: F.c. Van Geera, A.f. ZuurAbstract:This paper advocates an approach to extend single-Output Box-Jenkins transfer/noise models for several groundwater head series to a multiple-Output transfer/noise model. The approach links several groundwater head series and enables a spatial interpolation in terms of time series analysis. Our multiple-Output transfer/noise model relates the single-Output transfer/noise models from individual series by taking the spatial correlation of the white noise process into account anti spatially interpolating the parameters of the transfer and noise models. The parameters of the lime series models and the while noise process are considered to be spatial stochastic fields, and are described geostatistically. The model parameters and the noise variance are interpolated by means of Kriging. The approach's applicability is illustrated by two cases: a point study (an observation well with measured data from seven observation screens) and an area study that requires spatial interpolation. These show the method's usefulness for examining the effectiveness of monitoring strategies, filling in missing data and for the quality control of measured data. It also enables the responses of the single-Output transfer/noise models to be spatially interpolated.
