Name
Abstract | ||
|---|---|---|
We propose in this paper a new prediction paradigm, which is based on filter banks for subband decomposition of the sequences to be predicted. Filter banks allow the implementation of a parallel computing system, taking the advantage of a faster and more accurate implementation. In particular, we introduce a novel subband decomposition method yielding baseband sequences that are easier to be predicted. The core of the prediction system is based on a neural model, which is trained for each subband using specific embedding techniques. The latter are used in order to optimize the prediction performances when dealing with real-world data sequences, which often possess a chaotic behavior. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/CIMCA.2006.57 | CIMCA/IAWTIC |
Keywords | Field | DocType |
neural prediction,subband decomposition,chaotic behavior,baseband filter banks,parallel computing system,new prediction paradigm,filter bank,prediction performance,baseband sequence,novel subband decomposition method,prediction system,accurate implementation,decomposition method,parallel computer,neural nets | Sequence prediction,Baseband,Embedding,Pattern recognition,Computer science,Decomposition method (constraint satisfaction),Data sequences,Artificial intelligence,Chaotic,Artificial neural network,Machine learning,Prediction system | Conference |
ISBN | Citations | PageRank |
0-7695-2731-0 | 0 | 0.34 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Panella, M. | 1 | 3 | 0.71 |
| Alfred A. Rizzi | 2 | 1208 | 179.03 |