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Event-Driven State Estimation for Stochastic Networked Systems | 10.39 MB
Title: Event-Driven State Estimation for Stochastic Networked Systems
Author: Cong Huang;Peng Mei;Hamid Reza Karimi;
Category: Nonfiction, Science & Nature, Technology, Engineering
Language: English | 248 Pages | ISBN: 0443450145
Description:
Event-Driven State Estimation for Stochastic Networked Systems offers a comprehensive and clear explanation of recent developments in event-based state estimation for stochastic systems within limited communication networks, bringing together existing and emerging concepts. It provides a series of the latest results in, including but not limited to, recursive state estimation, fusion estimation, and state and fault estimation. The book provides practitioner readers with practical tools for the analysis and design of stochastic systems under limited communication networks, capturing recent advances in theory, technological aspects and real-world applications of advanced event-based state estimation methodologies. Realistic research problems are addressed in each chapter, with numerical and simulation results provided to reflect engineering practice, while demonstrating the main focus of the developed estimation approaches. The book is an advanced-level resource presented in an accessible manner, appealing to senior students as a core reference and to researchers and practitioners alike - Summarizes the latest research concepts, conclusions and applications of event-based state estimation methodologies for stochastic systems under limited communication networks - Addresses the analysis and design of various types of stochastic systems under event-triggered mechanisms - Utilizing state estimation strategies, challenges such as recursive state estimation, fusion estimation, and state and fault estimation for different stochastic systems are explored - Utilizing state estimation strategies, the book challenges such as recursive state estimation, fusion estimation, and state and fault estimation for different stochastic systems are explored
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