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Hu / Jia / Wang

Variance-Constrained Filtering for Stochastic Complex Systems

Theories and Algorithms

Medium: Buch
ISBN: 978-981-962636-6
Verlag: Springer Nature Singapore
Erscheinungstermin: 30.04.2025
Lieferfrist: bis zu 10 Tage

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.


Produkteigenschaften


  • Artikelnummer: 9789819626366
  • Medium: Buch
  • ISBN: 978-981-962636-6
  • Verlag: Springer Nature Singapore
  • Erscheinungstermin: 30.04.2025
  • Sprache(n): Englisch
  • Auflage: Erscheinungsjahr 2025
  • Serie: Intelligent Control and Learning Systems
  • Produktform: Gebunden
  • Gewicht: 716 g
  • Seiten: 310
  • Format (B x H x T): 160 x 241 x 23 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

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.