Name
Abstract | ||
|---|---|---|
High Energy Physics (HEP) experiments require discrimination of a few interesting events among a huge number of background events generated during an experiment. Hierarchical triggering hardware architectures are needed to perform this tasks in real-time. In this paper three neural network models are studied as possible candidate for such systems. A modified Multi-Layer Perception (MLP) architecture and a E alpha Net architecture are compared against a traditional MLP Test error below 25% is archived by all architectures in two different simulation strategies. E alpha Net performance are 1 to 2% better on test error with respect to the other two architectures using the smaller network topology. The design of a digital implementation of the proposed neural network is also outlined. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/1-4020-3432-6_18 | BIOLOGICAL AND ARTIFICIAL INTELLIGENCE ENVIRONMENTS |
Keywords | Field | DocType |
neural networks,intelligent data analysis,embedded neural networks | Architecture,Experimental data,Computer science,Network topology,Artificial intelligence,Artificial neural network,Perceptron,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.50 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salvatore Vitabile | 1 | 444 | 60.03 |
| Giovanni Pilato | 2 | 258 | 58.71 |
| Giorgio Vassallo | 3 | 122 | 21.04 |
| Sabato Marco Siniscalchi | 4 | 310 | 30.21 |
| Antonio Gentile | 5 | 63 | 10.63 |
| Filippo Sorbello | 6 | 218 | 29.48 |