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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

Mrugalski, Marcin

Hardcover

Series: Studies in Computational Intelligence, Book 510

Technology & EngineeringGeneral Computers

ISBN10: 331901546X
ISBN13: 9783319015460
Publisher: Springer Nature
Published: Aug 19 2013
Pages: 182
Weight: 1.03
Height: 0.50 Width: 6.14 Depth: 9.21
Language: English

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.

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