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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis [electronic resource] / by Marcin Mrugalski.

By: Mrugalski, Marcin [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 510Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XXI, 182 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319015477.Subject(s): Engineering | Artificial intelligence | Computational intelligence | Complexity, Computational | Control engineering | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | Complexity | ControlAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- Designing of dynamic neural networks -- Estimation methods in training of ANNs for robust fault diagnosis -- MLP in robust fault detection of static non-linear systems -- GMDH networks in robust fault detection of dynamic non-linear systems -- State-space GMDH networks for actuator robust FDI.
In: Springer eBooksSummary: The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practical applications.  .
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Introduction -- Designing of dynamic neural networks -- Estimation methods in training of ANNs for robust fault diagnosis -- MLP in robust fault detection of static non-linear systems -- GMDH networks in robust fault detection of dynamic non-linear systems -- State-space GMDH networks for actuator robust FDI.

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practical applications.  .

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