Multi-model jumping systems robust filtering and fault detection /
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 fil...
| Main Author: | He, Shuping. |
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| Other Authors: | Luan, Xiaoli., SpringerLink (Online service) |
| Format: | eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
2021.
Singapore : 2021. |
| Physical Description: |
1 online resource (xiii, 182 pages) : illustrations. |
| Subjects: |
| Summary: |
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. |
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| Item Description: |
Introduction -- Robust filtering for multi-model jumping system -- Finite-time robust filtering for multi-model jumping system -- Finite-frequency robust filtering for multi-model jumping system -- Higher order moment robust filtering for multi-model jumping system -- Robust fault detection for multi-model jumping system -- Observer-based robust fault detection for fuzzy multi-model jumping system -- Filtering-based robust fault detection of fuzzy multi-model jumping system -- Neural network-based robust fault detection for nonlinear multi-model jumping system -- Conclusion. 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. Includes bibliographical references. Access restricted to registered UOB users with valid accounts. |
| Physical Description: |
1 online resource (xiii, 182 pages) : illustrations. |
| Bibliography: |
Includes bibliographical references. |
| ISBN: |
9789813364745 9813364742 |
| Access: |
Access restricted to registered UOB users with valid accounts. |