𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Artificial neural networks in Space Station optimal attitude control

✍ Scribed by Renjith R. Kumar; Hans Seywald; Samir M. Deshpande; Zia Rahman

Publisher
Elsevier Science
Year
1995
Tongue
English
Weight
862 KB
Volume
35
Category
Article
ISSN
0094-5765

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

Artificial neural networks in process es
Artificial neural networks in process estimation and control
✍ M.J. Willis; G.A. Montague; C. Di Massimo; M.T. Tham; A.J. Morris πŸ“‚ Article πŸ“… 1992 πŸ› Elsevier Science 🌐 English βš– 692 KB

In this contribution, the suitability of the artificial neural network methodology for solving some process engineering problems is discussed. First the concepts involved in the formulation of artificial neural networks are presented. Next the suitability of the technique to provide estimates of dif

Optimal control of non-linear systems th
Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques
✍ TomΓ‘s MartΓ­nez-MarΓ­n; Pedro J. Zufiria πŸ“‚ Article πŸ“… 1999 πŸ› John Wiley and Sons 🌐 English βš– 128 KB πŸ‘ 2 views

In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a

Knowledge-based modularization and globa
Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting
✍ Gerald Corzo; Dimitri Solomatine πŸ“‚ Article πŸ“… 2007 πŸ› Elsevier Science 🌐 English βš– 847 KB

Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train sep

Stabilizing and robustifying the learnin
Stabilizing and robustifying the learning mechanisms of artificial neural networks in control engineering applications
✍ M. Onder Efe; Okyay Kaynak πŸ“‚ Article πŸ“… 2000 πŸ› John Wiley and Sons 🌐 English βš– 918 KB

This paper discusses the stabilizability of artificial neural networks trained by utilizing the gradient information. The method proposed constructs a dynamic model of the conventional update mechanism and derives the stabilizing values of the learning rate. The stability in this context corresponds