Forgetting memristor based neuromorphic system for pattern training and recognition

被引:29
|
作者
Zhang, Peijian [1 ]
Li, Chuandong [1 ]
Huang, Tingwen [2 ]
Chen, Ling [1 ]
Chen, Yiran [3 ]
机构
[1] Southwest Univ, Dept Elect & Informat Engn, Chongqing Key Lab Nonlinear Circuits & Intelligen, Chongqing 400715, Peoples R China
[2] Texas A&M Univ Qatar, Dept Math, Doha 23874, Qatar
[3] Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA 15261 USA
基金
美国国家科学基金会;
关键词
Memristors; Neuromorphic system; Crossbar; Pattern training and recognition; SYNAPSE; NETWORK; DEVICE;
D O I
10.1016/j.neucom.2016.10.012
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a neuromorphic system for mean variance based pattern training and recognition. The system contains a self-learning circuit, a training circuit and a recognition circuit. Memristor model with forgetting effect which has memory ability and forgetting effect simultaneously is applied to simulate forgetting mechanism of neuromorphic system. Different from previous work, which divided training circuit as off line process, here the weight-changing circuit and the recognition part are combined on line for pattern training and recognition. For illustration, the whole neuromorphic system is applied to recognize handwriting number '0-9' on gray images, and simulations verify its effectiveness.
引用
收藏
页码:47 / 53
页数:7
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