Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

✍ Scribed by Marcin Mrugalski (auth.)

Publisher: Springer International Publishing
Year: 2014
Tongue: English
Leaves: 196
Series: Studies in Computational Intelligence 510
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages 1-17
Introduction....Pages 1-7
Designing of Dynamic Neural Networks....Pages 9-46
Estimation Methods in Training of ANNs for Robust Fault Diagnosis....Pages 47-68
MLP in Robust Fault Detection of Static Non-linear Systems....Pages 69-92
GMDH Networks in Robust Fault Detection of Dynamic Non-linear Systems....Pages 93-124
State-Space GMDH Networks for Actuator Robust FDI....Pages 125-163
Conclusions and Future Research Directions....Pages 165-167
Back Matter....Pages 169-181

✦ Subjects

Computational Intelligence;Artificial Intelligence (incl. Robotics);Complexity;Control

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