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Hybrid Estimation of Complex Systems [electronic resource] / by Michael W. Hofbaur.

By: Contributor(s): Series: Lecture Notes in Control and Information Science ; 319Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Description: XIX, 148 p. 69 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783540315872
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No titleDDC classification:
  • 629.8 23
LOC classification:
  • TJ212-225
Online resources:
Contents:
Hybrid Estimation at a Glance -- Probabilistic Hybrid Automata -- Hybrid Estimation -- Case Studies -- Conclusion.
In: Springer eBooksSummary: This monograph provides a tool-set for hybrid estimation that can successfully monitor the behavior of complex artifacts with a large number of possible operational and failure modes such as production plants, automotive or aeronautic systems, and autonomous robots. For this purpose, ideas from the fields of System Theory and Artificial Intelligence are taken and hybrid estimation is reformulated as a search problem. This allows to focus the estimation onto highly probably operational modes, without missing symptoms that might be hidden among the noise in the system. Additionally a novel approach to continue hybrid estimation in the presence of unknown behavioral modes and to automate system analysis and synthesis tasks for on-line operation are presented. This leads to a flexible model-based hybrid estimation scheme for complex artifacts that robustly copes with unforeseen situations.
Item type: eBooks
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Hybrid Estimation at a Glance -- Probabilistic Hybrid Automata -- Hybrid Estimation -- Case Studies -- Conclusion.

This monograph provides a tool-set for hybrid estimation that can successfully monitor the behavior of complex artifacts with a large number of possible operational and failure modes such as production plants, automotive or aeronautic systems, and autonomous robots. For this purpose, ideas from the fields of System Theory and Artificial Intelligence are taken and hybrid estimation is reformulated as a search problem. This allows to focus the estimation onto highly probably operational modes, without missing symptoms that might be hidden among the noise in the system. Additionally a novel approach to continue hybrid estimation in the presence of unknown behavioral modes and to automate system analysis and synthesis tasks for on-line operation are presented. This leads to a flexible model-based hybrid estimation scheme for complex artifacts that robustly copes with unforeseen situations.

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