Real-time implementation of the cerebellum neural network

被引：0

作者：

Hao, Xinyu ^{[1
]}

Wang, Jiang ^{[1
]}

Yang, Shuangming ^{[1
]}

Deng, Bin ^{[1
]}

Wei, Xile ^{[1
]}

Yi, Guosheng ^{[1
]}

机构：

[1] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China

来源：

PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019) | 2019年

关键词：

Cerebellum; Field-programmable gate array (FPGA); Real-time; Robotic arm control; EFFICIENT FPGA IMPLEMENTATION; DYNAMICS; MODEL;

D O I：

10.1109/ccdc.2019.8832709

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The cerebellum is an important regulatory center for motor and learning in the human brain and its role has increasingly attracted attentions of researchers. Realizing a cerebellar model can be working on a biological time scale is very important both for exploration of mechanisms and practical application of the functions. In this study, we implement a cerebellum spiking neural network with an efficient method on field-programmable gate array (FPGA), which can generate the spiking activities in real time. Based on this, we propose an adaptive feedback control system with the cerebellum model. The dynamic error of robotic arm is taken as the system input and by using the learning mechanism of the cerebellum, the corresponding correction signal can be exported. The results show that this system can eliminate the error and control the robotic arm.

引用

页码：3595 / 3599

页数：5

共 50 条

[1] A NEURAL NETWORK IMPLEMENTATION FOR REAL-TIME SCENE ANALYSIS
BOOTH, R
ALLEN, CR
ADAMS, AE
[J]. FIRST IEE INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS, 1989, : 71 - 75
[2] Design and Implementation of a Neural Network for Real-Time Object Tracking
Ahmed, Javed
Jafri, M. N.
Ahmad, J.
Khan, Muhammad I.
[J]. PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 6, 2005, : 209 - 212
[3] Real-time implementation of ReSuMe learning in Spiking Neural Network
Xia, Yang
Uenohara, Seiji
Aihara, Kazuyuki
Levi, Timothee
[J]. ICAROB 2019: PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS, 2019, : 82 - 86
[4] Analog neural network implementation for a real-time surface classification application
Gatet, Laurent
Tap-Beteille, Helene
Lescure, Marc
[J]. IEEE SENSORS JOURNAL, 2008, 8 (7-8) : 1413 - 1421
[5] FPGA implementation of a neural network for a real-time hand tracking system
Krips, M
Lammert, T
Kummert, A
[J]. FIRST IEEE INTERNATION WORKSHOP ON ELECTRONIC DESIGN, TEST AND APPLICATIONS, PROCEEDINGS, 2002, : 313 - 317
[6] A neural network CMOS circuit implementation for real-time halftoning applications
Sadowski, Robert W.
Ballmann, Michael C.
Shoop, Barry L.
[J]. IEEE MWSCAS'06: PROCEEDINGS OF THE 2006 49TH MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL II, 2006, : 614 - +
[7] VLSI implementation of anisotropic probabilistic neural network for real-time image scaling
Chen, Ching-Han
Chang, Hsiang-Wen
Kuo, Chia-Ming
[J]. JOURNAL OF REAL-TIME IMAGE PROCESSING, 2019, 16 (01) : 71 - 80
[8] Design and implementation of a neural network controller for real-time adaptive voltage regulation
Yu, Xiao-Hua
Li, Weiming
Taufik
[J]. NEUROCOMPUTING, 2009, 73 (1-3) : 531 - 535
[9] REAL-TIME IMPLEMENTATION OF PROPAGATOR BEARING ESTIMATION ALGORITHM BY USE OF A NEURAL NETWORK
LUO, FL
BAO, Z
ZHAO, XP
[J]. IEEE JOURNAL OF OCEANIC ENGINEERING, 1992, 17 (04) : 320 - 325
[10] Real-time implementation of IPM motor protection using artificial neural network
Khan, M. A. S. K.
Rahman, M. A.
[J]. IECON 2007: 33RD ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-3, CONFERENCE PROCEEDINGS, 2007, : 1021 - 1026

← 1 2 3 4 5 →