Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

1. Front Matter

   Pages 1-17

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2. Introduction

   • Marcin Mrugalski

   Pages 1-7

3. Designing of Dynamic Neural Networks

   • Marcin Mrugalski

   Pages 9-46

4. Estimation Methods in Training of ANNs for Robust Fault Diagnosis

   • Marcin Mrugalski

   Pages 47-68

5. MLP in Robust Fault Detection of Static Non-linear Systems

   • Marcin Mrugalski

   Pages 69-92

6. GMDH Networks in Robust Fault Detection of Dynamic Non-linear Systems

   • Marcin Mrugalski

   Pages 93-124

7. State-Space GMDH Networks for Actuator Robust FDI

   • Marcin Mrugalski

   Pages 125-163

8. Conclusions and Future Research Directions

   • Marcin Mrugalski

   Pages 165-167

9. Back Matter

   Pages 169-181

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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.

 

• Computational Intelligence
• Fault Diagnosis Schemes
• Neural Networks
• Nonlinear Dynamic Systems
• Robustness
• complexity

From the reviews:

 

“The book deals with the use of artificial neural networks in robust fault diagnosis … . The ideas presented throughout the book are accompanied by examples and concrete applications. The book is devoted both to beginners in the field of fault diagnosis and advanced researchers in ANN model uncertainty.” (Smaranda Belciug, zbMATH, Vol. 1280, 2014)

• Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

  Marcin Mrugalski

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