An incremental learning algorithm for function approximation |
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| Authors: | Jacques M Bahi Sylvain Contassot-Vivier Marc Sauget |
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| Affiliation: | 1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing, China;2. Department of Geosciences, University of Oslo, N-0316 Oslo, 1047 Blindern, Norway |
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| Abstract: | This paper presents an incremental learning algorithm for feed-forward neural networks used as approximators of real world data. This algorithm allows neural networks of limited size to be obtained, providing better performances. The algorithm is compared to two of the main incremental algorithms (Dunkin and cascade correlation) in the respective contexts of synthetic data and of real data consisting of radiation doses in homogeneous environments. |
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