Name
Abstract | ||
|---|---|---|
In this paper we present and compare a selected set of neural network architectures used to detect a known signal corrupted by Gaussian and non-Gaussian noise components. We propose and simulate a new structure for the neural detector that uses a variable threshold in the decision stage. The practical implementation and the robustness of this neural detector are also considered |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/ISCAS.1999.777643 | Circuits and Systems, 1999. ISCAS '99. Proceedings of the 1999 IEEE International Symposium |
Keywords | DocType | Volume |
Gaussian noise,neural nets,signal detection,Gaussian noise components,decision stage,known signal detection,neural detectors,neural network architectures,nonGaussian noise components,robustness,variable threshold | Conference | 5 |
ISBN | Citations | PageRank |
0-7803-5471-0 | 3 | 0.48 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Burian, A. | 1 | 3 | 0.48 |
| Kuosmanen, P. | 2 | 17 | 1.60 |
| Saarinen, J. | 3 | 3 | 0.48 |