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Synopsis
The greatest original work on forecasting ever published. By a master of the post-Kalman era. Professor O'Reilly brings a lifetime’s engineering experience, and not a little scholarship, to an enduring problem. The result: a completely new theory of filtering and prediction for causal dynamical system models subject to significant disturbance uncertainty. Any causal dynamical system model can be used.
No a priori knowledge of the model uncertainties is required. Estimation of uncertain dynamical systems, it turns out, is a modelling problem. With necessary model validation. The criterion for high-fidelity signal reconstruction is how closely the signal estimates resemble the measured output data of the actual dynamical system.
In contradistinction to the Kalman off-line nominal design approach, the causal estimation approach is an on-line model tuning approach. This physical approach places estimation of dynamical systems on an experimental footing, akin to classical physics and engineering. And closer to present day industrial practice. Both causal and Kalman approaches are evaluated within twentieth century filtering and prediction theory. The new estimator is completely general, non-statistical, and very easy to use.
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About the Author
Professor John O'Reilly is Emeritus Professor of Engineering at the University of Glasgow, UK. One of the greatest engineers of his generation, Professor O'Reilly achieved international standing in several areas of estimation and control before moving to power systems and power electronics. His previous work includes New Directions in Dynamical Systems, Automatic Control and Singular Perturbations.
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Paperback. Condition: New. The greatest original work on forecasting ever published. By a master of the post-Kalman era. Professor O'Reilly brings a lifetime's engineering experience, and not a little scholarship, to an enduring problem. The result: a completely new theory of filtering and prediction for causal dynamical system models subject to significant disturbance uncertainty. Any causal dynamical system model can be used. No a priori knowledge of the model uncertainties is required. Estimation of uncertain dynamical systems, it turns out, is a modelling problem. With necessary model validation. The criterion for high-fidelity signal reconstruction is how closely the signal estimates resemble the measured output data of the actual dynamical system.In contradistinction to the Kalman off-line nominal design approach, the causal estimation approach is an on-line model tuning approach. This physical approach places estimation of dynamical systems on an experimental footing, akin to classical physics and engineering. And closer to present day industrial practice. Both causal and Kalman approaches are evaluated within twentieth century filtering and prediction theory. The new estimator is completely general, non-statistical, and very easy to use. Seller Inventory # LU-9781836282860
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Paperback. Condition: new. Paperback. The greatest original work on forecasting ever published. By a master of the post-Kalman era. Professor O'Reilly brings a lifetimes engineering experience, and not a little scholarship, to an enduring problem. The result: a completely new theory of filtering and prediction for causal dynamical system models subject to significant disturbance uncertainty. Any causal dynamical system model can be used. No a priori knowledge of the model uncertainties is required. Estimation of uncertain dynamical systems, it turns out, is a modelling problem. With necessary model validation. The criterion for high-fidelity signal reconstruction is how closely the signal estimates resemble the measured output data of the actual dynamical system.In contradistinction to the Kalman off-line nominal design approach, the causal estimation approach is an on-line model tuning approach. This physical approach places estimation of dynamical systems on an experimental footing, akin to classical physics and engineering. And closer to present day industrial practice. Both causal and Kalman approaches are evaluated within twentieth century filtering and prediction theory. The new estimator is completely general, non-statistical, and very easy to use. Professor O'Reilly brings a lifetimes engineering experience, and not a little scholarship, to an enduring problem. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781836282860
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