Networked Nonlinear Stochastic Time-Varying Systems. Questo articolo non è disponibile.
Lingua: inglese
Editore: CRC Press, 2024
- Brossura
- Nuovo

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Codice articolo POD-452192
- Titolo
- Networked Nonlinear Stochastic Time-Varying Systems
- Autore
- Dong, Hongli; Wang, Zidong; Hou, Nan
- Editore
- CRC Press
- Anno di pubblicazione
- 2024
- Condizione
- Brand New
- Rilegatura
- Brossura
- Lingua
- inglese
- ISBN 10
- 1032038802
- ISBN 13
- 9781032038803
Networked Non-linear Stochastic Time-Varying Systems: Analysis and Synthesis copes with the filter design, fault estimation and reliable control problems for different classes of nonlinear stochastic time-varying systems with network-enhanced complexities. Divided into three parts, the book discusses the finite-horizon filtering, fault estimation and reliable control, and randomly occurring nonlinearities/uncertainties followed by designing of distributed state and fault estimators, and distributed filters. The third part includes problems of variance-constrained H∞ state estimation, partial-nodes-based state estimation and recursive filtering for nonlinear time-varying complex networks with randomly varying topologies, and random coupling strengths.
- Offers a comprehensive treatment of the topics related to Networked Nonlinear Stochastic Time-Varying Systems with rigorous math foundation and derivation
- Unifies existing and emerging concepts concerning control/filtering/estimation and distributed filtering
- Provides a series of latest results by drawing on the conventional theories of systems science, control engineering and signal processing Deal with practical engineering problems such as event triggered H∞ filtering, non-fragile distributed estimation, recursive filtering, set-membership filtering
- Demonstrates illustrative examples in each chapter to verify the correctness of the proposed results
This book is aimed at engineers, mathematicians, scientists, and upper-level students in the fields of control engineering, signal processing, networked control systems, robotics, data analysis, and automation.
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