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...
Kaydedildi:
| Yazar: | |
|---|---|
| Materyal Türü: | Livre numérique |
| Dil: | Anglais |
| Baskı/Yayın Bilgisi: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer
[20..].
Cham : Springer Nature |
| Online Erişim: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Not: |
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 |
| LEADER | 03301nam a22003017a 4500 | ||
|---|---|---|---|
| 001 | 945773 | ||
| 008 | 130325q2000 xx ||| |||| 00| 0 eng d | ||
| 009 | PPN168327546 | ||
| 020 | |a 9783642348167 | ||
| 041 | 0 | |a eng | |
| 082 | |a 629.8 | ||
| 100 | 1 | |a Liu, Jinkun. | |
| 245 | 1 | 0 | |a Radial Basis Function (RBF) neural network control for mechanical systems : |b design, analysis and Matlab simulation |c by Jinkun Liu. |
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a 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 | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronautics | ||
| 776 | 0 | |t Radial Basis Function (RBF) Neural Network Control for Mechanical Systems |b Texte imprimé |z 9783642348150 | |
| 856 | 4 | |q PDF |u https://doi.org/10.1007/978-3-642-34816-7 |z Accès sur la plateforme de l'éditeur | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-SFC4S145-F |z Accès sur la plateforme Istex | |
| 856 | 4 | |5 452349901:747925968 |u https://ezproxy.univ-orleans.fr/login?url=https://doi.org/10.1007/978-3-642-34816-7 |z Accès Université d'Orléans | |
| 856 | 4 | |5 180339901:750938048 |u https://ezproxy.insa-cvl.fr/login?qurl=https://doi.org/10.1007/978-3-642-34816-7 |z Accès INSA CVL | |
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