Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis (Paperback, Softcover reprint of the original 1st ed. 2014)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 510
Release date:	August 2015
First published:	2014
Authors:	Marcin Mrugalski
Dimensions:	235 x 155 x 11mm (L x W x T)
Format:	Paperback
Pages:	182
Edition:	Softcover reprint of the original 1st ed. 2014
ISBN-13:	978-3-319-03286-3
Categories:	Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	3-319-03286-0
Barcode:	9783319032863

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