Name
Abstract | ||
|---|---|---|
Disruption is a sudden loss of magnetic confinement that can cause a damage of the machine walls and support structures. For this reason is of practical interest to be able to early detect the onset of the event. This paper presents a novel technique of early prediction of plasma disruption in Tokamak reactors which uses Neural Networks and Chaos theory. In particular, dynamical reconstruction and chaos theory have been considered for choosing the time window of prediction and to select the inputs set for the prediction system. Multi-Layer-Perceptron nets have been exploited for predicting the incoming of disruption. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/1-4020-3432-6_45 | Biological and Artificial Intelligence Environments |
Keywords | Field | DocType |
disruptions,Tokamaks,Chaos Theory | Tokamak,Magnetic confinement fusion,Computer science,Control theory,Artificial neural network,Chaos theory,Prediction system | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matteo Cacciola | 1 | 14 | 9.10 |
| Domenico Costantino | 2 | 0 | 0.68 |
| Antonino Greco | 3 | 8 | 3.63 |
| Francesco Carlo Morabito | 4 | 339 | 54.83 |
| Mario Versaci | 5 | 51 | 15.70 |