Springer Multi-model Jumping Systems: Robust Filtering and Fault Detection

Product Description This book focuses on multi-model systems, describing how to apply intelligent technologies to model complex multi-model systems by combining stochastic jumping system, neural network and fuzzy models. It focuses on robust filtering, including finite-time robust filtering, finite-frequency robust filtering and higher order moment robust filtering schemes, as well as fault detection problems for multi-model jump systems, such as observer-based robust fault detection, filtering-based robust fault detection and neural network-based robust fault detection methods. The book also demonstrates the validity and practicability of the theoretical results using simulation and practical examples, like circuit systems, robot systems and power systems. Further, it introduces readers to methods such as finite-time filtering, finite-frequency robust filtering, as well as higher order moment and neural network-based fault detection methods for multi-model jumping systems, allowing them to grasp the modeling, analysis and design of the multi-model systems presented and implement filtering and fault detection analysis for various systems, including circuit, network and mechanical systems.  From the Back Cover This book focuses on multi-model systems, describing how to apply intelligent technologies to model complex multi-model systems by combining stochastic jumping system, neural network and fuzzy models. It focuses on robust filtering, including finite-time robust filtering, finite-frequency robust filtering and higher order moment robust filtering schemes, as well as fault detection problems for multi-model jump systems, such as observer-based robust fault detection, filtering-based robust fault detection and neural network-based robust fault detection methods. The book also demonstrates the validity and practicability of the theoretical results using simulation and practical examples, like circuit systems, robot systems and power systems. Further, it introduces readers to methods such as finite-time filtering, finite-frequency robust filtering, as well as higher order moment and neural network-based fault detection methods for multi-model jumping systems, allowing them to grasp the modeling, analysis and design of the multi-model systems presented and implement filtering and fault detection analysis for various systems, including circuit, network and mechanical systems.  About the Author Shuping He is Full Professor at Anhui University, Hefei, China. From 2010 to 2011, he was Visiting Scholar at the Control Systems Centre, School of Electrical and Electronic Engineering, University of Manchester, UK. From 2011 to 2013, he was Senior Lecturer at Anhui University, Hefei, China, before becoming Professor at the School of Electrical Engineering and Automation there. He was a recipient of the Outstanding Youth Funds Award of Anhui Province in 2016, and the Honor of Excellent Young Talents in the University of Anhui Province in 2017. He won the Anhui Province (Rank 1) Natural Science Second Prize in 2017. He is also IEEE Senior Member. His current research interests include control theory & control systems; systems modeling, methods & applications; signal processing and artificial intelligence. He has co-authored a book on stochastic systems and authored or co-authored over 100 papers in professional journals, conference proceedings and technical reports.  Xiaoli Luan is Full Professor at the School of Internet of Things Engineering Jiangnan University, Wuxi, China, since 2017. From 2010 to 2011, she was Visiting Scholar at the University of Victoria, Australia. She was a recipient of the National Excellent Youth Funds Award and the “six talent peaks” Award of Jiangsu Province. Her current research interests include complex dynamic system modeling, advanced control and optimization, industrial big data analysis and applications, data mining and machine learning. She has authored or co-authored over