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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