A constrained optimization approach

Dawei Shi; Ling Shi; Tongwen Chen

doi:10.1007/978-3-319-26606-0_5

A constrained optimization approach

Dawei Shi^*, Ling Shi, Tongwen Chen

^*Corresponding author for this work

Department of Chemical and Biological Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Book Chapter › peer-review

Abstract

In the previous chapter, approximation techniques were utilized to exploit the information contained in the event-triggering sets for event-based estimator design. As has been mentioned earlier, the motivation of undertaking these approximations arises from the non-Gaussianity issue caused by the event-triggered measurement information, and the consequence is that the results and properties are obtained in an approximate sense, with the approximation errors difficult to evaluate.

Original language	English
Title of host publication	Studies in Systems, Decision and Control
Publisher	Springer International Publishing
Pages	77-108
Number of pages	32
DOIs	https://doi.org/10.1007/978-3-319-26606-0_5
Publication status	Published - 2016

Publication series

Name	Studies in Systems, Decision and Control
Volume	41
ISSN (Print)	2198-4182
ISSN (Electronic)	2198-4190

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

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10.1007/978-3-319-26606-0_5

Cite this

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title = "A constrained optimization approach",

abstract = "In the previous chapter, approximation techniques were utilized to exploit the information contained in the event-triggering sets for event-based estimator design. As has been mentioned earlier, the motivation of undertaking these approximations arises from the non-Gaussianity issue caused by the event-triggered measurement information, and the consequence is that the results and properties are obtained in an approximate sense, with the approximation errors difficult to evaluate.",

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AU - Shi, Ling

AU - Chen, Tongwen

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AB - In the previous chapter, approximation techniques were utilized to exploit the information contained in the event-triggering sets for event-based estimator design. As has been mentioned earlier, the motivation of undertaking these approximations arises from the non-Gaussianity issue caused by the event-triggered measurement information, and the consequence is that the results and properties are obtained in an approximate sense, with the approximation errors difficult to evaluate.

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UR - https://openalex.org/W2480095370

UR - https://www.scopus.com/pages/publications/85028964484

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BT - Studies in Systems, Decision and Control

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A constrained optimization approach

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