Advanced neural network-based computational schemes for robust fault diagnosis /

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...

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Bibliographic Details
Main Author: Mrugalski, Marcin (Author)
Format: Book
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2014
Cham : Springer, [2013]
Series:Studies in computational intelligence ; v. 510,
Studies in computational intelligence ; v.510,
Studies in computational intelligence, 510
Subjects:
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245 1 0 |a Advanced neural network-based computational schemes for robust fault diagnosis /  |c Marcin Mrugalski 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2014 
264 1 |a Cham :  |b Springer,  |c [2013] 
264 4 |c ©2014 
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490 1 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 510 
490 1 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v v. 510 
490 1 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v v.510 
504 |a Includes bibliographical references and index 
505 0 |a 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 
505 0 0 |t Designing of dynamic neural networks --  |t Estimation methods in training of ANNs for robust fault diagnosis --  |t MLP in robust fault detection of static non-linear systems --  |t GMDH networks in robust fault detection of dynamic non-linear systems --  |t State-space GMDH networks for actuator robust FDI 
520 |a 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 
588 0 |a Online resource; title from PDF title page (SpringerLink, viewed August 20, 2013) 
650 0 |a Artificial intelligence 
650 0 |a Engineering 
650 0 |a Fault location (Engineering) 
650 0 |a Neural networks (Computer science) 
650 0 |a Physics 
650 0 |a Robust control 
650 7 |a Fault location (Engineering)  |2 fast 
650 7 |a Ingénierie  |2 eclas 
650 7 |a Ingénierie  |2 eclas 
650 7 |a Neural networks (Computer science)  |2 fast 
650 7 |a Robust control  |2 fast 
650 1 4 |a Engineering 
650 2 4 |a Artificial Intelligence (incl. Robotics) 
650 2 4 |a Complexity 
650 2 4 |a Computational Intelligence 
650 2 4 |a Control 
653 0 |a Artificial Intelligence (incl. Robotics) 
653 0 |a Complexity 
653 0 |a Computational Intelligence 
653 0 |a Control 
653 0 |a Engineering 
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776 0 8 |i Printed edition:  |z 9783319015460 
830 0 |a Studies in computational intelligence ;  |v v. 510,  |x 1860-949X 
830 0 |a Studies in computational intelligence ;  |v v.510,  |x 1860-949X 
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