Discrete-Time High Order Neural Control Trained with Kalman Filtering
Synopsis
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.Book details
- Edition:
- 2008
- Series:
- Studies in Computational Intelligence
- Author:
- Edgar N. Sanchez, Alma Y. Alanís, Alexander G. Loukianov
- ISBN:
- 9783540782896
- Related ISBNs:
- 9783540782889
- Publisher:
- Springer Berlin Heidelberg
- Pages:
- N/A
- Reading age:
- Not specified
- Includes images:
- No
- Date of addition:
- 2022-06-19
- Usage restrictions:
- Copyright
- Copyright date:
- 2008
- Copyright by:
- N/A
- Adult content:
- No
- Language:
- English
- Categories:
- Computers and Internet, Earth Sciences, Mathematics and Statistics, Nonfiction, Science, Technology