Name
Title | ||
|---|---|---|
Learning stepsize selection for the geodesic-based neural blind deconvolution algorithm |
Abstract | ||
|---|---|---|
The present paper illustrates a geodesic-based learning algorithm over a curved parameter space for blind deconvolution application. The chosen deconvolving structure appears as a single neuron model whose learning rule arises from criterion-function minimization over a smooth manifold. In particular, we propose here a learning stepsize selection theory for the algorithm at hand. We consider the blind deconvolution performances of the algorithm as well as its computational burden. Also, a numerical comparison with seven blind-deconvolution algorithms known from the scientific literature is illustrated and discussed. Results of numerical tests conducted on a noiseless as well as a noisy system will confirm that the algorithm discussed in the present paper performs in a satisfactory way. Also, the performances of the presented algorithm will be compared with those exhibited by other blind deconvolution algorithms known from the literature. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4634042 | Neural Networks, 2008. IJCNN 2008. |
Keywords | Field | DocType |
deconvolution,differential geometry,learning (artificial intelligence),neural nets,criterion-function minimization,curved parameter space,geodesic-based neural blind deconvolution algorithm,stepsize selection learning theory | Signal processing,Blind deconvolution,Computer science,Deconvolution,Minification,Artificial intelligence,Artificial neural network,Algorithm design,Pattern recognition,Algorithm,Learning rule,Machine learning,Geodesic | Conference |
ISSN | ISBN | Citations |
1098-7576 E-ISBN : 978-1-4244-1821-3 | 978-1-4244-1821-3 | 0 |
PageRank | References | Authors |
0.34 | 5 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Fiori | 1 | 494 | 52.86 |