Central Library

Normal view MARC view ISBD view

Event-Based State Estimation [electronic resource] : A Stochastic Perspective / by Dawei Shi, Ling Shi, Tongwen Chen.

By: Shi, Dawei [author.].
Contributor(s): Shi, Ling [author.] | Chen, Tongwen [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Systems, Decision and Control: 41Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XIII, 208 p. 37 illus., 32 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319266060.Subject(s): Engineering | System theory | Probabilities | Control engineering | Power electronics | Engineering | Control | Probability Theory and Stochastic Processes | Power Electronics, Electrical Machines and Networks | Systems Theory, ControlAdditional physical formats: Printed edition:: No titleDDC classification: 629.8 Online resources: Click here to access online
Contents:
Introduction -- Linear Gaussian Systems and Event-Based State Estimation -- Event-Triggered Sampling -- Approximate Optimal Filtering Approaches -- Constrained Optimization Approach -- Set-Valued Filtering Approach -- Probabilistic Approach -- Communications Rate Analysis -- Open Problems -- Appendices: Brief Review of Probability Theory; Linear Estimation Theory.
In: Springer eBooksSummary: This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed. The self-contained presentation makes this book suitable for readers with no more than a basic knowledge of probability analysis, matrix algebra and linear systems. The introduction and literature review provide information, while the main content deals with estimation problems from four distinct angles in a stochastic setting, using numerous illustrative examples and comparisons. The text elucidates both theoretical developments and their applications, and is rounded out by a review of open problems. This book is a valuable resource for researchers and students who wish to expand their knowledge and work in the area of event-triggered systems. At the same time, engineers and practitioners in industrial process control will benefit from the event-triggering technique that reduces communication costs and improves energy efficiency in wireless automation applications. .
    average rating: 0.0 (0 votes)
No physical items for this record

Introduction -- Linear Gaussian Systems and Event-Based State Estimation -- Event-Triggered Sampling -- Approximate Optimal Filtering Approaches -- Constrained Optimization Approach -- Set-Valued Filtering Approach -- Probabilistic Approach -- Communications Rate Analysis -- Open Problems -- Appendices: Brief Review of Probability Theory; Linear Estimation Theory.

This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed. The self-contained presentation makes this book suitable for readers with no more than a basic knowledge of probability analysis, matrix algebra and linear systems. The introduction and literature review provide information, while the main content deals with estimation problems from four distinct angles in a stochastic setting, using numerous illustrative examples and comparisons. The text elucidates both theoretical developments and their applications, and is rounded out by a review of open problems. This book is a valuable resource for researchers and students who wish to expand their knowledge and work in the area of event-triggered systems. At the same time, engineers and practitioners in industrial process control will benefit from the event-triggering technique that reduces communication costs and improves energy efficiency in wireless automation applications. .

There are no comments for this item.

Log in to your account to post a comment.