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- Toujours un magasin près de chez vous
Discrete-Time High Order Neural Control
Trained with Kalman Filtering
Edgar N Sanchez, Alma Y Alanís, Alexander G Loukianov
105,45 €
+ 210 points
Format
Description
Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks, controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem, nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations.
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 110
- Langue:
- Anglais
- Collection :
- Tome:
- n° 112
Caractéristiques
- EAN:
- 9783540782889
- Date de parution :
- 29-04-08
- Format:
- Livre relié
- Format numérique:
- Genaaid
- Dimensions :
- 156 mm x 234 mm
- Poids :
- 349 g
