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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.
| Autorius: | Xiaoli Luan, Shuping He, |
| Leidėjas: | Springer Nature Singapore |
| Išleidimo metai: | 2022 |
| Knygos puslapių skaičius: | 196 |
| ISBN-10: | 9813364769 |
| ISBN-13: | 9789813364769 |
| Formatas: | 235 x 155 x 11 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Multi-model Jumping Systems: Robust Filtering and Fault Detection“
