@inproceedings{f19df221d92b4484a35d8e4bfa366f46,
title = "Improving the convergence of backpropagation by opposite transfer functions",
abstract = "The backpropagation algorithm is a very popular approach to learning in feed-forward multi-layer perceptron networks. However, in many scenarios the time required to adequately learn the task is considerable. Many existing approaches have improved the convergence rate by altering the learning algorithm. We present a simple alternative approach inspired by opposition-based learning that simultaneously considers each network transfer function and its opposite. The effect is an improvement in convergence rate and over traditional backpropagation learning with momentum. We use four common benchmark problems to illustrate the improvement in convergence time.",
author = "Mario Ventresca and Tizhoosh, {Hamid R.}",
year = "2006",
doi = "10.1109/ijcnn.2006.247153",
language = "English (US)",
isbn = "0780394909",
series = "IEEE International Conference on Neural Networks - Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4777--4784",
booktitle = "International Joint Conference on Neural Networks 2006, IJCNN '06",
note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",
}