Training Recurrent Neural Network for Nonlinear Adaptive Channel Equalization with Differential Evolution

Sumeth Yuenyong; AKINORI NISHIHARA

論文・著書情報

タイトル

和文:
英文:	Training Recurrent Neural Network for Nonlinear Adaptive Channel Equalization with Differential Evolution

著者

和文:	YUENYONGSUMETH, 西原明法.
英文:	Sumeth Yuenyong, AKINORI NISHIHARA.

言語

English

掲載誌/書名

和文:
英文:	Proceedings of 2013 RISP International Workshop on Nonlinear Circuits, Communication and Signal Processing

巻, 号, ページ

Vol. 1 No. 1 pp. 409-411

出版年月

2013年3月4日

出版者

和文:
英文:

会議名称

和文:
英文:	2013 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing

開催地

和文:
英文:	Big Island, Hawaii

アブストラクト

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).

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