Name
Abstract | ||
|---|---|---|
Neural networks are intended to be robust to noise and tolerant to failures in their architecture. Therefore, these systems are particularly interesting to be integrated in hardware and to be operating under noisy environment. In this work, measurements are introduced which can decrease the sensitivity of Radial Basis Function networks to noise without any degradation in their approximation capability. For this purpose, pareto-optimal solutions are determined for the parameters of the network. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11840817_103 | ICANN (1) |
Keywords | Field | DocType |
approximation capability,noisy environment,neural network,pareto-optimal noise,pareto-optimal solution,radial basis function network,approximation property,rbf network | Radial basis function network,Mathematical optimization,Radial basis function,Computer science,Pareto optimal,Artificial intelligence,Artificial neural network,Machine learning | Conference |
Volume | ISSN | ISBN |
4131 | 0302-9743 | 3-540-38625-4 |
Citations | PageRank | References |
1 | 0.41 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ralf Eickhoff | 1 | 65 | 12.37 |
| U. Rückert | 2 | 755 | 103.61 |