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Variance-constrained filtering for s...

Hu, Jun.

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Variance-constrained filtering for stochastic complex systems = theories and algorithms /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Variance-constrained filtering for stochastic complex systems/ by Jun Hu, Zidong Wang, Chaoqing Jia.
Reminder of title:	theories and algorithms /
Author:	Hu, Jun.
other author:	Wang, Zidong.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xv, 310 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Recursive Filtering and Boundedness Analysis with ROQ -- Resilient Filtering with Stochastic Uncertainties and Incomplete Measurements -- Event-Triggered Resilient Filtering with Stochastic Uncertainties and SPDs -- Event-triggered Filtering with Missing Measurements -- Fault Estimation Against Randomly Occurring Deception Attacks -- Fault Estimation with Packet Dropouts and ROUs -- Fault Estimation with Randomly Occurring Faults and Sensor Saturations -- State Estimation for Complex Networks with Missing Measurements -- Quantized State Estimation for Complex Networks with Uncertain Inner Coupling -- Event-Based State Estimation for Complex Networks under UOPs -- Event-Based State Estimation for Complex Networks with Fading Observations and UST -- State Estimation for Complex Networks with Uncertain Observations and Coupling Strength -- Conclusions and Future Work.
Contained By:	Springer Nature eBook
Subject:	Filters (Mathematics) -
Online resource:	https://doi.org/10.1007/978-981-96-2637-3
ISBN:	9789819626373

Variance-constrained filtering for stochastic complex systems = theories and algorithms /
Hu, Jun.

Variance-constrained filtering for stochastic complex systemstheories and algorithms /[electronic resource] :by Jun Hu, Zidong Wang, Chaoqing Jia. - Singapore :Springer Nature Singapore :2025. - xv, 310 p. :ill., digital ;24 cm. - Intelligent control and learning systems,v. 182662-5466 ;. - Intelligent control and learning systems ;v. 18..

Introduction -- Recursive Filtering and Boundedness Analysis with ROQ -- Resilient Filtering with Stochastic Uncertainties and Incomplete Measurements -- Event-Triggered Resilient Filtering with Stochastic Uncertainties and SPDs -- Event-triggered Filtering with Missing Measurements -- Fault Estimation Against Randomly Occurring Deception Attacks -- Fault Estimation with Packet Dropouts and ROUs -- Fault Estimation with Randomly Occurring Faults and Sensor Saturations -- State Estimation for Complex Networks with Missing Measurements -- Quantized State Estimation for Complex Networks with Uncertain Inner Coupling -- Event-Based State Estimation for Complex Networks under UOPs -- Event-Based State Estimation for Complex Networks with Fading Observations and UST -- State Estimation for Complex Networks with Uncertain Observations and Coupling Strength -- Conclusions and Future Work.

This book is concerned with the variance-constrained optimized filtering problems and their potential applications for nonlinear time-varying dynamical systems. The distinguished features of this book are highlighted as follows. (1) A unified framework is provided for handling the variance-constrained filtering problems of nonlinear time-varying dynamical systems with incomplete information. (2) The application potentials of variance-constrained optimized filtering in networked time-varying dynamical systems are outlined. It contains some new concepts, new models and new methodologies with practical significance in control engineering and signal processing. It is a collection of several research results and thereby serves as a useful reference for upper undergraduate, postgraduate and engineers who are interested in studying (i) the variance-constrained filtering, (ii) recent advances affected by incomplete information and (iii) potential applications in practical engineering systems.

ISBN: 9789819626373

Standard No.: 10.1007/978-981-96-2637-3doiSubjects--Topical Terms:

556522
Filters (Mathematics)

LC Class. No.: QA297

Dewey Class. No.: 515.642

Variance-constrained filtering for stochastic complex systems = theories and algorithms /
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245 1 0 $a Variance-constrained filtering for stochastic complex systems $h [electronic resource] : $b theories and algorithms / $c by Jun Hu, Zidong Wang, Chaoqing Jia.
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490 1 $a Intelligent control and learning systems, $x 2662-5466 ; $v v. 18
505 0 $a Introduction -- Recursive Filtering and Boundedness Analysis with ROQ -- Resilient Filtering with Stochastic Uncertainties and Incomplete Measurements -- Event-Triggered Resilient Filtering with Stochastic Uncertainties and SPDs -- Event-triggered Filtering with Missing Measurements -- Fault Estimation Against Randomly Occurring Deception Attacks -- Fault Estimation with Packet Dropouts and ROUs -- Fault Estimation with Randomly Occurring Faults and Sensor Saturations -- State Estimation for Complex Networks with Missing Measurements -- Quantized State Estimation for Complex Networks with Uncertain Inner Coupling -- Event-Based State Estimation for Complex Networks under UOPs -- Event-Based State Estimation for Complex Networks with Fading Observations and UST -- State Estimation for Complex Networks with Uncertain Observations and Coupling Strength -- Conclusions and Future Work.
520 $a This book is concerned with the variance-constrained optimized filtering problems and their potential applications for nonlinear time-varying dynamical systems. The distinguished features of this book are highlighted as follows. (1) A unified framework is provided for handling the variance-constrained filtering problems of nonlinear time-varying dynamical systems with incomplete information. (2) The application potentials of variance-constrained optimized filtering in networked time-varying dynamical systems are outlined. It contains some new concepts, new models and new methodologies with practical significance in control engineering and signal processing. It is a collection of several research results and thereby serves as a useful reference for upper undergraduate, postgraduate and engineers who are interested in studying (i) the variance-constrained filtering, (ii) recent advances affected by incomplete information and (iii) potential applications in practical engineering systems.
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