@inproceedings{f57a7438a6f2463e9eb581a500d49d22,
title = "A reliable resilient backpropagation method with gradient ascent",
abstract = "While the Resilient Backpropagation (RPROP) method can be extremely fast in converging to a solution, it suffers from the local minima problem. In this paper, a fast and reliable learning algorithm for multi-layer artificial neural networks is proposed. The learning model has two phases: the RPROP phase and the gradient ascent phase. The repetition of two phases can help the network get out of local minima. The proposed algorithm is tested on some benchmark problems. For all the above problems, the systems are shown to be capable of escaping from the local minima and converge faster than the Backpropagation with momentum algorithm and the simulated annealing techniques.",
author = "Xugang Wang and Hongan Wang and Guozhong Dai and Zheng Tang",
year = "2006",
doi = "10.1007/978-3-540-37275-2_31",
language = "英語",
isbn = "3540372741",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "236--244",
booktitle = "Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings",
note = "International Conference on Intelligent Computing, ICIC 2006 ; Conference date: 16-08-2006 Through 19-08-2006",
}