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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