Discrete-Time High Order Neural Control : Trained with Kalman Filtering PDF
by Edgar N. Sanchez, Alma Y. Alanis, Alexander G. Loukianov
Part of the Studies in Computational Intelligence series
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.
Information
-
Download - Immediately Available
- Format:PDF
- Publisher:Springer Berlin Heidelberg
- Publication Date:24/06/2008
- Category:
- ISBN:9783540782896
Other Formats
- Paperback / softback from £95.55
- Hardback from £95.55
Information
-
Download - Immediately Available
- Format:PDF
- Publisher:Springer Berlin Heidelberg
- Publication Date:24/06/2008
- Category:
- ISBN:9783540782896
