Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes
An unappealing characteristic of all real-world systems is the fact that they are vulnerable to faults, malfunctions and, more generally, unexpected modes of - haviour. This explains why there is a continuous need for reliable and universal monitoring systems based on suitable and e?ective fault dia...
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Serie: | Lecture Notes in Control and Information Sciences
377 |
| Soggetti: | |
| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Artificial neural networks for the modelling and fault diagnosis of technical processes, Krzysztof Patan, Berlin, Springer, 2008, 1 vol. (XXII-245 p.), Lecture notes in control and information sciences, 978-3-540-79871-2 |
Sommario:
- Modelling Issue in Fault Diagnosis Locally Recurrent Neural Networks Approximation Abilities of Locally Recurrent Networks Stability and Stabilization of Locally Recurrent Networks Optimum Experimental Design for Locally Recurrent Networks Decision Making in Fault Detection Industrial Applications Concluding Remarks and Further Research Directions