Name
Abstract | ||
|---|---|---|
The prediction of future values of environmental data sequences is mandatory to the cost-effective management of available resources. Consequently, the possibility to improve the prediction accuracy is a very important goal to be pursued. We propose in the present paper two possible approaches for refining the prediction accuracy on real data sequences. Both these approaches make use of Mixture of Gaussian neural networks for the solution of suitable function approximation problems. The first approach pursues the regularization of the learning process based on the reconstructed state of the context delivering the sequence; the second one is based on the particular chaotic nature of the prediction error. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1016/S0925-2312(03)00392-8 | Neurocomputing |
Keywords | Field | DocType |
Environmental data prediction,Phase-space prediction,Twofold prediction,Mixture of Gaussian neural networks | Data mining,Function approximation,Computer science,Regularization (mathematics),Artificial intelligence,Environmental data,Artificial neural network,Chaotic,Refining (metallurgy),Mean squared prediction error,Pattern recognition,Gaussian,Machine learning | Journal |
Volume | Issue | ISSN |
55 | 3 | 0925-2312 |
Citations | PageRank | References |
11 | 1.12 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Massimo Panella | 1 | 368 | 30.24 |
| Alfred A. Rizzi | 2 | 1208 | 179.03 |
| G. Martinelli | 3 | 11 | 1.46 |