Name
Title | ||
|---|---|---|
Real-Time Independent Component Analysis Based on Gradient Learning with Simultaneous Perturbation Stochastic Approximation |
Abstract | ||
|---|---|---|
We present a novel algorithm for independent component analysis (ICA) based on gradient learning with simultaneous perturbation stochastic approximation (SPSA). This algorithm can work well both in batch mode and in on-line mode of ICA processing. It converges very fast even for non-stationary, and/or non-identically independent distributed (non-I.I.D.) signals, so that the algorithm is very suitable for most real-time applications. In this paper, theories and implementations of the algorithm are described. Results of computer simulation are also presented to demonstrate the effectiveness. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30133-2_47 | LECTURE NOTES IN COMPUTER SCIENCE |
Keywords | Field | DocType |
computer simulation,real time,independent component analysis | Mathematical optimization,Blind deconvolution,Simultaneous perturbation stochastic approximation,Computer science,Algorithm,Independent component analysis,Batch processing,Stochastic approximation | Conference |
Volume | ISSN | Citations |
3214 | 0302-9743 | 1 |
PageRank | References | Authors |
0.39 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuxue Ding | 1 | 235 | 33.84 |
| Jie Huang | 2 | 33 | 4.47 |
| Daming Wei | 3 | 215 | 44.97 |
| Sadao Omata | 4 | 17 | 3.20 |