Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis (Hardcover)

Name: Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis
Author: Marcin Mrugalski

von Marcin Mrugalski Verlag: Springer International Publishing

2014 - Erschienen 19.08.2013 - Gebunden, 204 Seiten, 241mm x 160mm x 16mm, Sprache(n): eng

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Devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemesDetails neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systemsTreats 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 robustnessThe book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field

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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📚 Bücher

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MARZIES Buch- und Medienhandel

Bestell-Nr.:

A21655535

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ISBN:

331901546X

EAN: