| タイトル | Training Recurrent Neural Network for Nonlinear Adaptive Channel Equalization with Differential Evolution |
| その他のタイトル | Training Recurrent Neural Network for Nonlinear Adaptive Channel Equalization with Differential Evolution |
| 参考URL | http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100653011 |
| 著者（日） | 西原, 明法 |
| 著者（英） | YUENYONG, SUMETH; Yuenyong, Sumeth; NISHIHARA, AKINORI |
| 発行日 | 2013-04-08 |
| 刊行物名 | Proceedings of 2013 RISP International Workshop on Nonlinear Circuits, Communication and Signal Processing |
| 巻 | 1 |
| 号 | No. 1 |
| 開始ページ | 409 |
| 終了ページ | 411 |
| 刊行年月日 | 2013-03 |
| 言語 | en |
| 内容記述 | Recurrent neural network (RNN) had been applied for equal- ization of nonlinear communication channel. However the error surface of RNN contains local minima, so a gradient de- scent algorithm can easily get stuck and produce sub-optimal solution. A global optimization algorithm called Differen- tial Evolution (DE) was used to train RNN for this task and shown to achieve better result than the gradient-based Real Time Recurrent Learning (RTRL). |
| 資料種別 | Conference Paper |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/607732 |
