Recurrent Neural Networks for Local Models Prediction
| Autor: | Cherif, Aymen, Boné, Romuald, Cardot, Hubert |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique Fondamentale et Appliquée de Tours (LIFAT), Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Maurel, Denis |
| Jazyk: | angličtina |
| Rok vydání: | 2009 |
| Předmět: |
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] |
| Zdroj: | ICCN International Conference on Cognitive Neurodynamics International Conference on Cognitive Neurodynamics, 2009, Hangzhou, China |
| ISSN: | 2325-2383 |
| Popis: | International audience; "Local models" (Walter, J., et al. International Joint Conference on Neural Networks, vol. 1. (1990) 589-594), consists on dividing the data into homogeneous clusters by Vector Quantization (VQ (Gray, R. M., and Neuhoff, D.L. IEEE Trans. Inf. Theory 44(6) (1998) 2325-2383)) to simplify the prediction task on each cluster and mostly inspired from the Self-Organizing Maps algorithm (SOM (Kohonen, T. Self-Organization and associative memory, 3rd edn. (1989))). Since recurrent neural networks have demonstrated in many times a better results and specially for chaotic time series (Boné, R. Recurrent Neural Networks for Time Series Forecasting. (2000)), we propose in this paper a method to use the Recurrent Neural Networks in the local approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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