Name
Abstract | ||
|---|---|---|
For polarized signals, which arise in many application fields, a statistical framework in terms of quaternionic random processes is proposed. Based on it, the ability of real-, complex- and quaternionic- valued multi-layer perceptrons (MLPs) of performing classification tasks for such signals is evaluated. For the multi-dimensional neural networks the relevance of class label representations is discussed. For signal to noise separation it is shown that the quaternionic MLP yields an optimal solution. Results on the classification of two different polarized signals are also reported. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1142/S0129065708001403 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
quaternionic-valued neural networks, complex-valued neural networks, multi-dimensional neural computation, multi-layer perceptrons, polarized signals | Multi layer,Pattern recognition,Computer science,Signal-to-noise ratio,Stochastic process,Signal classification,Artificial intelligence,Artificial neural network,Perceptron,Machine learning | Journal |
Volume | Issue | ISSN |
18 | 2 | 0129-0657 |
Citations | PageRank | References |
33 | 1.23 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sven Buchholz | 1 | 33 | 1.23 |
| Nicolas Le Bihan | 2 | 254 | 23.35 |