A Parallel Incremental Learning Algorithm for Neural Networks with Fault Tolerance
High Performance Computing for Computational Science - VECPAR 2008
An incremental learning algorithm for function approximation
Advances in Engineering Software
Neural network based algorithm for radiation dose evaluation in heterogeneous environments
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Gridification of a radiotherapy dose computation application with the xtremweb-CH environment
GPC'11 Proceedings of the 6th international conference on Advances in grid and pervasive computing
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The purpose of this work is to further study the relevance of accelerating the Monte Carlo calculations for the gamma rays external radiotherapy through feed-forward neural networks. We have previously presented a parallel incremental algorithm that builds neural networks of reduced size, while providing high quality approximations of the dose deposit. Our parallel algorithm consists in a regular decomposition of the initial learning dataset (also called learning domain) in as much subsets as available processors. However, the initial learning set presents heterogeneous signal complexities and consequently, the learning times of regular subsets are very different. This paper presents an efficient learning domain decomposition which balances the signal complexities across the processors. As will be shown, the resulting irregular decomposition allows for important gains in learning time of the global network.