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Refined instrumental variable methods of recursive time-series analysis Part III. Extensions

Refined instrumental variable methods of recursive time-series analysis Part III. Extensions This is the final paper in a series of three which have been concerned with the comprehensive evaluation of the refined instrumental variable (IV) method of recursive time-series analysis. The paper shows how the refined IV procedure can be extended in various important directions and how it can provide the basis for the synthesis of optimal generalized equation error (GEE) algorithms for a wide class of stochastic dynamic systems. The topics discussed include the estimation of parameters in continuous-time differential equation models from continuous or discrete data; the estimation of time-variable parameters in continuous or discrete-time models of dynamic systems ; the design of stochastic state reconstruction (Wiener-Kalman) filters direct from data ; the estimation of parameters in multi-input, single output (MISO) transfer function models ; the design of simple stochastic approximation (SA) implementations of the refined IV algorithms ; and the use of the recursive algorithms in self-adaptive (self tuning) control. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Control Taylor & Francis

Refined instrumental variable methods of recursive time-series analysis Part III. Extensions

International Journal of Control , Volume 31 (4): 24 – Apr 1, 1980

Refined instrumental variable methods of recursive time-series analysis Part III. Extensions

International Journal of Control , Volume 31 (4): 24 – Apr 1, 1980

Abstract

This is the final paper in a series of three which have been concerned with the comprehensive evaluation of the refined instrumental variable (IV) method of recursive time-series analysis. The paper shows how the refined IV procedure can be extended in various important directions and how it can provide the basis for the synthesis of optimal generalized equation error (GEE) algorithms for a wide class of stochastic dynamic systems. The topics discussed include the estimation of parameters in continuous-time differential equation models from continuous or discrete data; the estimation of time-variable parameters in continuous or discrete-time models of dynamic systems ; the design of stochastic state reconstruction (Wiener-Kalman) filters direct from data ; the estimation of parameters in multi-input, single output (MISO) transfer function models ; the design of simple stochastic approximation (SA) implementations of the refined IV algorithms ; and the use of the recursive algorithms in self-adaptive (self tuning) control.

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References (15)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1366-5820
eISSN
0020-7179
DOI
10.1080/00207178008961080
Publisher site
See Article on Publisher Site

Abstract

This is the final paper in a series of three which have been concerned with the comprehensive evaluation of the refined instrumental variable (IV) method of recursive time-series analysis. The paper shows how the refined IV procedure can be extended in various important directions and how it can provide the basis for the synthesis of optimal generalized equation error (GEE) algorithms for a wide class of stochastic dynamic systems. The topics discussed include the estimation of parameters in continuous-time differential equation models from continuous or discrete data; the estimation of time-variable parameters in continuous or discrete-time models of dynamic systems ; the design of stochastic state reconstruction (Wiener-Kalman) filters direct from data ; the estimation of parameters in multi-input, single output (MISO) transfer function models ; the design of simple stochastic approximation (SA) implementations of the refined IV algorithms ; and the use of the recursive algorithms in self-adaptive (self tuning) control.

Journal

International Journal of ControlTaylor & Francis

Published: Apr 1, 1980

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