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Reduced Order Systems [electronic resource] /by Ali A. Jalali, Craig S. Sims†, Parviz Famouri.

by Jalali, Ali A [author.]; Sims†, Craig S [author.]; Famouri, Parviz [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Control and Information Science: 343Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.Description: XIII, 202 p. Also available online. online resource.ISBN: 9783540343592.Subject(s): Engineering | Finance | Systems theory | Engineering | Control Engineering | Systems Theory, Control | Quantitative FinanceOnline resources: Click here to access online
Contents:
Introduction -- Static Problems -- Stationary Processes -- Estimation Over Finite Time Intervals -- Smoothing -- Stochastic Control over Finite Time Intervals -- Innovation Approach to Reduced Order Estimation -- Linear Two-Point Boundary Value Processes -- Reduced Order Filtering for Flexible Space Sructures -- Robust Reduced Order Filtering.
In: Springer eBooksSummary: This monograph presents a detailed and unified treatment of the theory of reduced order systems. Covered topics include reduced order modeling, reduced order estimation, reduced order control, and the design of reduced order compensators for stochastic systems. Special emphasis is placed on optimization using a quadratic performance criterion. Both continuous and discrete time linear dynamical systems are considered, and state space system representation is used throughout the book. It provides a coherent view of the recent theory of reduced order theory and its applications including a wide range of application problems, solutions and unresolved issues.
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Introduction -- Static Problems -- Stationary Processes -- Estimation Over Finite Time Intervals -- Smoothing -- Stochastic Control over Finite Time Intervals -- Innovation Approach to Reduced Order Estimation -- Linear Two-Point Boundary Value Processes -- Reduced Order Filtering for Flexible Space Sructures -- Robust Reduced Order Filtering.

This monograph presents a detailed and unified treatment of the theory of reduced order systems. Covered topics include reduced order modeling, reduced order estimation, reduced order control, and the design of reduced order compensators for stochastic systems. Special emphasis is placed on optimization using a quadratic performance criterion. Both continuous and discrete time linear dynamical systems are considered, and state space system representation is used throughout the book. It provides a coherent view of the recent theory of reduced order theory and its applications including a wide range of application problems, solutions and unresolved issues.

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