東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

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 /
LDR:02998nmm a2200337 a 4500 001 2410410
003 DE-He213
005 20250430130231.0
006 m d
007 cr nn 008maaau
008 260204s2025 si s 0 eng d
020 $a 9789819626373 $q (electronic bk.)
020 $a 9789819626366 $q (paper)
024 7 $a 10.1007/978-981-96-2637-3 $2 doi
035 $a 978-981-96-2637-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA297
072 7 $a GPFC $2 bicssc
072 7 $a MAT034000 $2 bisacsh
072 7 $a GPFC $2 thema
082 0 4 $a 515.642 $2 23
090 $a QA297 $b .H874 2025
100 1 $a Hu, Jun. $3 2132561
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.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xv, 310 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Filters (Mathematics) $3 556522
650 0 $a Observers (Control theory) $3 1566011
650 1 4 $a Dynamical Systems. $3 3538746
650 2 4 $a Applied Dynamical Systems. $3 3538870
650 2 4 $a Control and Systems Theory. $3 3381515
650 2 4 $a Systems Theory, Control. $3 893834
700 1 $a Wang, Zidong. $3 2132562
700 1 $a Jia, Chaoqing. $3 3784275
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Intelligent control and learning systems ; $v v. 18. $3 3784276
856 4 0 $u https://doi.org/10.1007/978-981-96-2637-3
950 $a Mathematics and Statistics (SpringerNature-11649)