An adaptive penalty-based learning extension for backpropagation and its variants

フォーマット:

論文

責任表示:

Boris, Jansen ; Nakayama, Kenji

言語:

英語

出版情報:

IEEE = Institute of Electrical and Electronics Engineers, 2006-07-01

著者名:

• Boris, Jansen
• Nakayama, Kenji

掲載情報:

IEEE International Conference on Neural Networks - Conference Proceedings

開始ページ:

6427

終了ページ:

6432

バージョン:

publisher

概要:

金沢大学理工研究域　電子情報学系<br />Over the years, many improvements and refinements of the backpropagation learning algorithm have been reported. In this paper, a new adaptive penalty-based learning extension for the backpropagation learning algo … rithm and its variants is proposed. The new method initially puts pressure on artificial neural networks in order to get all outputs for all training patterns into the correct half of the output range, instead of mainly focusing on minimizing the difference between the target and actual output values. The technique is easy to implement and computationally inexpensive. In this study, the new approach has been applied to the backpropagation learning algorithm as well as the RPROP learning algorithm and simulations have been performed. The superiority of the new proposed method is demonstrated. By applying the extension, the number of successful runs can be greatly increased and the average number of epochs to convergence can be well reduced on various problem instances. Furthermore, the change of the penalty values during training has been studied and its observation shows the active role the penalties play within the learning process. © 2006 IEEE. 続きを見る

URL:

http://hdl.handle.net/2297/18164