Associate learning and correcting in a memristive neural network
This paper further studies the ability of the associate learning and self-correcting in a memristive artificial neural network (ANN).Different from the existing models, the present ANN contains the multiply-threshold neurons, the discrete charge-controlled memristors, and a new learning law named the max-input-feedback (MIF).We shall demonstrate the processes of the associative learning and associative correcting via a modified Pavlov experiment where more conditioning factors are considered.We also make some comparisons of MIF with spike-timing- dependent plasticity and back-propagation and show that MIF learning law is suitable to fast learning.
Memristor Associate learning and correcting Max-input-feedback law
Ling Chen Chuandong Li Xin Wang Shukai Duan
国内会议
西南大学2014年全国博士生学术论坛(电子技术与信息科学领域)
重庆
英文
278-283
2014-12-01（万方平台首次上网日期，不代表论文的发表时间）