Radial Basis Function (RBF) neural network control for mechanical systems : design, analysis and Matlab simulation
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design metho...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | Livre numérique |
| اللغة: | Anglais |
| منشور في: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer
[20..].
Cham : Springer Nature |
| الوصول للمادة أونلاين: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| ملاحظة: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, Texte imprimé, 9783642348150 |
جدول المحتويات:
- Introduction RBF Neural Network Design and Simulation RBF Neural Network Control Based on Gradient Descent Algorithm Adaptive RBF Neural Network Control Neural Network Sliding Mode Control Adaptive RBF Control Based on Global Approximation Adaptive Robust RBF Control Based on Local Approximation Backstepping Control with RBF Digital RBF Neural Network Control Discrete Neural Network Control Adaptive RBF Observer Design and Sliding Mode Control