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Title
Japanese: 
English:Training Recurrent Neural Network for Nonlinear Adaptive Channel Equalization with Differential Evolution 
Author
Japanese: YUENYONGSUMETH, 西原明法.  
English: Sumeth Yuenyong, AKINORI NISHIHARA.  
Language English 
Journal/Book name
Japanese: 
English:Proceedings of 2013 RISP International Workshop on Nonlinear Circuits, Communication and Signal Processing 
Volume, Number, Page Vol. 1    No. 1    pp. 409-411
Published date Mar. 4, 2013 
Publisher
Japanese: 
English: 
Conference name
Japanese: 
English:2013 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing 
Conference site
Japanese: 
English:Big Island, Hawaii 
Abstract 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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