Andere Kunden interessierten sich auch für
![Neural Advances in Processing Nonlinear Dynamic Signals]( "Neural Advances in Processing Nonlinear Dynamic Signals")
Neural Advances in Processing Nonlinear Dynamic Signals117,99 €![Recent Metaheuristic Computation Schemes in Engineering]( "Recent Metaheuristic Computation Schemes in Engineering")
Recent Metaheuristic Computation Schemes in Engineering117,99 €![Neural Advances in Processing Nonlinear Dynamic Signals]( "Neural Advances in Processing Nonlinear Dynamic Signals")
Neural Advances in Processing Nonlinear Dynamic Signals117,99 €![Recent Metaheuristic Computation Schemes in Engineering]( "Recent Metaheuristic Computation Schemes in Engineering")
Recent Metaheuristic Computation Schemes in Engineering171,19 €![Robustness in Econometrics]( "Robustness in Econometrics")
Robustness in Econometrics126,99 €![New Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics]( "New Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics")
New Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics147,99 €![Multilayer Neural Networks]( "Multilayer Neural Networks")
Multilayer Neural Networks74,99 €
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.
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.
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)
"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)