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.
Produkteigenschaften
- Artikelnummer: 9783540782889
- Medium: Buch
- ISBN: 978-3-540-78288-9
- Verlag: Springer Berlin Heidelberg
- Erscheinungstermin: 29.04.2008
- Sprache(n): Englisch
- Auflage: 2008
- Serie: Studies in Computational Intelligence
- Produktform: Gebunden, HC runder Rücken kaschiert
- Gewicht: 354 g
- Seiten: 110
- Format (B x H x T): 160 x 241 x 12 mm
- Ausgabetyp: Kein, Unbekannt
Themen
- Mathematik | Informatik
- EDV | Informatik
- Angewandte Informatik
- Computeranwendungen in Wissenschaft & Technologie
- Mathematik | Informatik
- Mathematik
- Numerik und Wissenschaftliches Rechnen
- Angewandte Mathematik, Mathematische Modelle