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
相关论文
共 50 条
  • [41] Memristor Crossbar Based Multicore Neuromorphic Processors
    Taha, Tarek M.
    Hasan, Raqibul
    Yakopcic, Chris
    2014 27TH IEEE INTERNATIONAL SYSTEM-ON-CHIP CONFERENCE (SOCC), 2014, : 383 - 388
  • [42] Neuromorphic Electronic Module Based on the Use of the Memristor Electronic-Component Base for Image Recognition
    E. A. Ryndin
    I. A. Mavrin
    N. V. Andreeva
    V. V. Luchinin
    Nanobiotechnology Reports, 2023, 18 : S194 - S202
  • [43] Neuromorphic Electronic Module Based on the Use of the Memristor Electronic-Component Base for Image Recognition
    Ryndin, E. A.
    Mavrin, I. A.
    Andreeva, N. V.
    Luchinin, V. V.
    NANOBIOTECHNOLOGY REPORTS, 2023, 18 (SUPPL 1) : S194 - S202
  • [44] Training memristor-based multilayer neuromorphic networks with SGD, momentum and adaptive learning rates
    Yan, Zheng
    Chen, Jiadong
    Hu, Rui
    Huang, Tingwen
    Chen, Yiran
    Wen, Shiping
    NEURAL NETWORKS, 2020, 128 (142-149) : 142 - 149
  • [45] Analysis of Parasitic Effects in a Crossbar in CMOS Based Neuromorphic System for Pattern Recognition Using Memristive Synapses
    Thomas, Sherin A.
    Vohra, Sahibia Kaur
    Kumar, Rahul
    Sharma, Rohit
    Das, Devarshi Mrinal
    IEEE TRANSACTIONS ON NANOTECHNOLOGY, 2022, 21 : 380 - 389
  • [46] Impact of Memristor Defects in a Neuromorphic Radionuclide Identification System
    Canales-Verdial, Jorge, I
    Woods, Walt
    Teuscher, Christof
    Osinski, Marek
    Zarkesh-Ha, Payman
    2020 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2020,
  • [47] Simulation platform for pattern recognition based on reservoir computing with memristor networks
    Gouhei Tanaka
    Ryosho Nakane
    Scientific Reports, 12
  • [48] Simulation platform for pattern recognition based on reservoir computing with memristor networks
    Tanaka, Gouhei
    Nakane, Ryosho
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [49] Ex-situ Training of Dense Memristor Crossbar for Neuromorphic Applications
    Hasan, Raqibul
    Yakopcic, Chris
    Taha, Tarek M.
    PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL SYMPOSIUM ON NANOSCALE ARCHITECTURES (NANOARCH 15), 2015, : 75 - 81
  • [50] Neuromorphic Pattern Recognition Using SET Technology
    Chen, Xuejun
    Wang, Zhongmin
    Zhang, Zhigang
    Huang, Juyi
    DCABES 2006 PROCEEDINGS, VOLS 1 AND 2, 2006, : 502 - 504