Documents disponibles écrits par cet auteur

An Exponentially Weighted Moving Average Method for Identification and Monitoring of / Shyh-Hong Hwang in Industrial & engineering chemistry research, Vol. 47 n°21 (Novembre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 n°21 (Novembre 2008) . - p. 8239–8249 Titre : An Exponentially Weighted Moving Average Method for Identification and Monitoring of Type de document : texte imprimé Auteurs : Shyh-Hong Hwang, Auteur ; Ho-Tsen Chen, Auteur ; Chuei-Tin Chang, Auteur Année de publication : 2008 Article en page(s) : p. 8239–8249 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : The  stochasticEWMA  parameter  estimators Résumé : To identify parametric models for stochastic systems, the standard least-squares method tends to yield biased parameter estimates owing to correlated residuals resulting from unknown stochastic disturbances. Although the consistency properties of parameter estimates could generically be secured by instrumental variable methods, the inadequate choices of instruments and prefilters would render them much less efficient. This article establishes a method to identify an ARARX (AutoRegressive AutoRegressive with eXogenous input), an ARMAX (AutoRegressive Moving Average with eXogenous input), or a BJ (Box–Jenkins) model based on the process output data smoothed by the EWMA (Exponentially Weighted Moving Average). The major advantages of the method are 2-fold. First, the proposed off-line and online algorithms often acquire unbiased, efficient, and consistent parameter estimation from identification tests operating in open loop or closed loop. Second, the resultant process plus disturbance model can be easily employed to remove the autocorrelation in process data for accurate statistical process monitoring. Monte-Carlo simulation studies demonstrate that the proposed method provides reliable parametric models for a wide variety of noise characteristics and is highly robust with respect to the sampling period, sample size, and noise-to-signal ratio. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0707218 [article] An Exponentially Weighted Moving Average Method for Identification and Monitoring of [texte imprimé] / Shyh-Hong Hwang, Auteur ; Ho-Tsen Chen, Auteur ; Chuei-Tin Chang, Auteur . - 2008 . - p. 8239–8249. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°21 (Novembre 2008) . - p. 8239–8249 Mots-clés : The  stochasticEWMA  parameter  estimators Résumé : To identify parametric models for stochastic systems, the standard least-squares method tends to yield biased parameter estimates owing to correlated residuals resulting from unknown stochastic disturbances. Although the consistency properties of parameter estimates could generically be secured by instrumental variable methods, the inadequate choices of instruments and prefilters would render them much less efficient. This article establishes a method to identify an ARARX (AutoRegressive AutoRegressive with eXogenous input), an ARMAX (AutoRegressive Moving Average with eXogenous input), or a BJ (Box–Jenkins) model based on the process output data smoothed by the EWMA (Exponentially Weighted Moving Average). The major advantages of the method are 2-fold. First, the proposed off-line and online algorithms often acquire unbiased, efficient, and consistent parameter estimation from identification tests operating in open loop or closed loop. Second, the resultant process plus disturbance model can be easily employed to remove the autocorrelation in process data for accurate statistical process monitoring. Monte-Carlo simulation studies demonstrate that the proposed method provides reliable parametric models for a wide variety of noise characteristics and is highly robust with respect to the sampling period, sample size, and noise-to-signal ratio. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0707218

Iterative identification of continuous-time hammerstein and wiener systems using a two-stage estimation algorithm / Ho-Tsen Chen in Industrial & engineering chemistry research, Vol. 48 N°3 (Février 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N°3 (Février 2009) . - p. 1495–1510 Titre : Iterative identification of continuous-time hammerstein and wiener systems using a two-stage estimation algorithm Type de document : texte imprimé Auteurs : Ho-Tsen Chen, Auteur ; Shyh-Hong Hwang, Auteur ; Chuei-Tin Chang, Auteur Année de publication : 2009 Article en page(s) : p. 1495–1510 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Nonlinear  processes  Hammerstein  Systems  Wiener  Systems Résumé : This article presents an iterative method to deal with the identification of continuous-time single-input/single-output Hammerstein and Wiener systems, characterized by a series connection of a nonlinear static element and a linear dynamic element. The internal variable between the nonlinear and linear elements is inaccessible to measurements so that simultaneous parameter estimation of the two elements cannot be easily achieved in a least-squares fashion. This difficulty could be circumvented by updating the internal variable at each iteration step. A two-stage estimation algorithm, in conjunction with moving-horizon smoothing and a solution-guiding mechanism, is established to ensure the convergence and accuracy of the iterative method in the face of linear structure mismatch, high static nonlinearity with an unknown characteristic, and severe noise. At the first stage, a good description of the static nonlinearity is given by a multisegment function or a polynomial in an iterative manner. Linear structure mismatch is allowed for this stage of estimation. At the second stage, the identification problem is reduced to a simple linear one with the internal variable gained at the first stage. A noniterative procedure can then be applied to determine accurately the structure and parameters of the linear dynamic element. Studies with simulated and experimental examples demonstrate that the proposed identification method is valid for a wide variety of nonlinear system dynamics and test conditions. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800149w [article] Iterative identification of continuous-time hammerstein and wiener systems using a two-stage estimation algorithm [texte imprimé] / Ho-Tsen Chen, Auteur ; Shyh-Hong Hwang, Auteur ; Chuei-Tin Chang, Auteur . - 2009 . - p. 1495–1510. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N°3 (Février 2009) . - p. 1495–1510 Mots-clés : Nonlinear  processes  Hammerstein  Systems  Wiener  Systems Résumé : This article presents an iterative method to deal with the identification of continuous-time single-input/single-output Hammerstein and Wiener systems, characterized by a series connection of a nonlinear static element and a linear dynamic element. The internal variable between the nonlinear and linear elements is inaccessible to measurements so that simultaneous parameter estimation of the two elements cannot be easily achieved in a least-squares fashion. This difficulty could be circumvented by updating the internal variable at each iteration step. A two-stage estimation algorithm, in conjunction with moving-horizon smoothing and a solution-guiding mechanism, is established to ensure the convergence and accuracy of the iterative method in the face of linear structure mismatch, high static nonlinearity with an unknown characteristic, and severe noise. At the first stage, a good description of the static nonlinearity is given by a multisegment function or a polynomial in an iterative manner. Linear structure mismatch is allowed for this stage of estimation. At the second stage, the identification problem is reduced to a simple linear one with the internal variable gained at the first stage. A noniterative procedure can then be applied to determine accurately the structure and parameters of the linear dynamic element. Studies with simulated and experimental examples demonstrate that the proposed identification method is valid for a wide variety of nonlinear system dynamics and test conditions. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800149w