Partial Moments in System Identification
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Beschreibung
This book provides a complete round-up of developments concerned with the application of partial moments in system identification and data-driven modelling; it captures the essence of work carried out at the Laboratoire d'Informatique et d'Automatique pour les Systèmes for more than 40 years.
The book begins with introductory material, describing both the mathematical tools associated with partial moments and reinitialized partial moments and an example demonstrating their use. The authors then proceed to show how these tools can be used for the identification of continuous-time linear models, discrete-time linear models, continuous-time linear state-space models, linear parameter-varying models and multidimensional models based on partial differential equations. The properties and performances of each of these approaches are presented. The analogy with algebraic approaches is proved, thus opening perspectives for extension to other fields. The text removes some long-standing limitations on the implementation of partial-moment-based tools in system identification.
This book is of interest to researchers and postgraduates studying system identification, control theory, applied mathematics and computer science. It is also useful for engineers working on industrial applications of parametric estimation of mathematical models.
This book provides a complete round-up of developments concerned with the application of partial moments in system identification and data-driven modelling; it captures the essence of work carried out at the Laboratoire d'Informatique et d'Automatique pour les Systèmes for more than 40 years.
The book begins with introductory material, describing both the mathematical tools associated with partial moments and reinitialized partial moments and an example demonstrating their use. The authors then proceed to show how these tools can be used for the identification of continuous-time linear models, discrete-time linear models, continuous-time linear state-space models, linear parameter-varying models and multidimensional models based on partial differential equations. The properties and performances of each of these approaches are presented. The analogy with algebraic approaches is proved, thus opening perspectives for extension to other fields. The text removes some long-standing limitations on the implementation of partial-moment-based tools in system identification.
This book is of interest to researchers and postgraduates studying system identification, control theory, applied mathematics and computer science. It is also useful for engineers working on industrial applications of the parametric estimation of mathematical models.
Removes previous limitations on implementation of partial-moment-based tools in system identification Shows how parametric estimation tools can be adapted to very broad classes of model Provides preliminary steps for the initialization of non-convex-optimization algorithms
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Themen in »Partial Moments in System Identification«
System Identification Data-driven Modelling Partial Moment Reinitialized Partial Moment Continuous-time Linear Models Discrete-time Linear Models Continuous-time Linear State-space Models Partial Differential Equation Models Algebraic Identification
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“The unified framework for system identification using partial moments and reinitialized partial moments is presented. The book reflects results gained during at least four decades of research. … The book is suitable for the control theory specialists, applied mathematicians developing parameter estimation/PDE identification tools and doctoral students in control engineering, applied mathematics, or signal processing.” (Denis Sidorov, zbMATH 1569.93002, 2026)
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