Memristor-based neural network circuit with weighted sum simultaneous perturbation training and its applications

NEUROCOMPUTING

卷:462

Article

Xu, Cong¹  Wang, Chunhua¹  Sun, Yichuang²  Hong, Qinghui¹  Deng, Quanli¹  Chen, Haowen¹ 

[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Hunan, Peoples R China

[2] Univ Hertfordshire, Sch Engn & Technol, Hatfield AL10 9AB, Herts, England

关键词: Memristor;    Neural network;    Synaptic weight;    Circuit design;    Recognition;    Weighted sum simultaneous perturbation;   

DOI  :  10.1016/j.neucom.2021.08.072

来源: Elsevier

【 摘 要 】

In this work, a full circuit of memristor-based neural network with weighted sum simultaneous perturbation training is proposed. Firstly, a synaptic circuit is designed by using a pair of memristors, which can represent negative, zero, and positive synaptic weights. Secondly, a full circuit of the neural network is designed, with all operations being completed on the circuit without any computer aid. The neural network is trained with the weighted sum simultaneous perturbation algorithm. The algorithm does not involve complex derivative calculation and error back propagation, and it only applies perturbations to weighted sum, so the circuit implementation is more simple. Finally, application simulations of the proposed neural network circuit are performed via PSpice. The results of simulation indicate that the memristor-based neural network is practical and effective. (c) 2021 Elsevier B.V. All rights reserved.

【 授权许可】

Free