1.

Record Nr.

UNINA9910299482403321

Autore

Mrugalski Marcin

Titolo

Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis / / by Marcin Mrugalski

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014

ISBN

3-319-01547-8

Edizione

[1st ed. 2014.]

Descrizione fisica

1 online resource (XXI, 182 p. 125 illus.)

Collana

Studies in Computational Intelligence, , 1860-949X ; ; 510

Disciplina

006.3/2

Soggetti

Computational intelligence

Artificial intelligence

Computational complexity

Control engineering

Computational Intelligence

Artificial Intelligence

Complexity

Control and Systems Theory

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Bibliographic Level Mode of Issuance: Monograph

Nota di bibliografia

Includes bibliographical references (pages 169-179) and index.

Nota di contenuto

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.

Sommario/riassunto

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.  .