Variance-Constrained Filtering for Stochastic Complex Systems
Description
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.
Reviews / Votes
"This book is concerned with variance-constrained filtering problems and their applications for investigating some classes of time-varying dynamical systems with incomplete information. . This book is a research monograph whose intended audience is engineers, mathematicians, scientists, and graduate and postgraduate students in the fields of control engineering, signal processing, networked control systems, robotics, data analysis, and automation." (Mykhailo Moklyachuk, Mathematical Reviews, March, 2026)
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Persons
Prof. Jun Hu received the B.Sc. degree in Information and Computing Science and M.Sc. degree in Applied Mathematics from Harbin University of Science and Technology, Harbin, China, in 2006 and 2009, respectively, and the Ph.D. degree in Control Science and Engineering from Harbin Institute of Technology, Harbin, China, in 2013. From September 2010 to September 2012, he was a Visiting Ph.D. student in the Department of Information Systems and Computing, Brunel University London, London, U.K. From May 2014 to April 2016, he was an Alexander von Humboldt research fellow at the University of Kaiserslautern, Kaiserslautern, Germany. From January 2018 to January 2021, he was a research fellow at University of South Wales, Pontypridd, U.K. He is with the Department of Applied Mathematics and School of Automation, Harbin University of Science and Technology, Harbin, China. His research interests include nonlinear control, filtering and fault estimation, time-varying systems and complex networks. He has published more than 90 papers in refereed international journals. Prof. Hu serves as a reviewer for Mathematical Reviews, as an editor for Neurocomputing, Neural Processing Letters, Systems Science & Control Engineering, and as a guest editor for International Journal of General Systems and Information Fusion.
Prof. Zidong Wang received the B.Sc. degree in Mathematics in 1986 from Suzhou University, Suzhou, China, the M.Sc. degree in Applied Mathematics and the Ph.D. degree in Electrical Engineering both from Nanjing University of Science and Technology, Nanjing, China, in 1990 and 1994, respectively. He is currently a Professor of Dynamical Systems and Computing in the Department of Computer Science, Brunel University London, Uxbridge, U.K. From 1990 to 2002, he held teaching and research appointments in universities in China, Germany, and the U.K. Prof. Wang's research interests include dynamical systems, signal processing, bioinformatics, control theory and applications. He has published a number of papers in international journals. He is a holder of the Alexander von Humboldt Research Fellowship of Germany, the JSPS Research Fellowship of Japan, and the William Mong Visiting Research Fellowship of Hong Kong.
Prof. Wang serves (or has served) as the Editor-in-Chief for International Journal of Systems Science, the Editor-in-Chief for Neurocomputing, the Editor-in-Chief for Systems Science & Control Engineering, and an Associate Editor for 12 international journals, including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Neural Networks, IEEE Transactions on Signal Processing, and IEEE Transactions on Systems, Man, and Cybernetics--Part C. He is a Member of the Academia Europaea, a Member of the European Academy of Sciences and Arts, an Academician of the International Academy for Systems and Cybernetic Sciences, a Fellow of the IEEE, a Fellow of the Royal Statistical Society, and a member of program committee for many international conferences.
Dr. Chaoqing Jia received the B.Sc. degree in Information and Computing Science, the M.Sc. degree in Mathematics and the Ph.D. degree in Operational Research and Cybernetics from Harbin University of Science and Technology, in 2015, 2019 and 2022, respectively. From February 2024 to February 2025, he was an academic visitor in the Department of Information Systems and Computing, Brunel University London, London, U.K. He is with the School of Automation, Harbin University of Science and Technology, Harbin, China. His research interests include time-varying systems, complex networks, variance-constrained state estimation. He has published 15 papers in refereed international journals. Dr. Jia serves as a reviewer for Automatica, Neurocomputing, Neural Processing Letters and International Journal of Systems Science.
Content
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.
