Spiking Neural Networks on Self-updating System-on-chip for Autonomous Control

被引：0
|
作者：
Zhou, Yimin [1 ]
Krundel, Ludovic [1 ]
Mulvaney, David [1 ]
Chouliaras, Vassilios [1 ]
Xia, Xu [2 ]
Li, Guohui [3 ]
机构：
[1] Univ Loughborough, Dept Elect & Elect Engn, Loughborough LE11 3TU, Leics, England
[2] ALPC Energy Technol, Beijing 100083, Peoples R China
[3] TEDA Orking HiTech CO LTD, Tianjin 300072, Peoples R China
来源：
2011 INTERNATIONAL CONFERENCE ON COMPUTERS, COMMUNICATIONS, CONTROL AND AUTOMATION (CCCA 2011), VOL III | 2010年
基金：
英国工程与自然科学研究理事会;
关键词：
neural networks; rule learning; cellular automata;
D O I：
暂无
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 artificial intelligence (AI) technique has suffered in solving its computationally hard problems in recent years. In this paper, a self-upgrading autonomous system is designed to tackle end-to-end AI-hard problems and to achieve self adapting communication via modular and hierarchical extension from linguistic and semiotic constructs. A system-on a-chip (SoC) self-adaptive control system can learn arbitrary shape of the robot body or machine parts. Simulation results have proved the effectiveness of learning abilities of the proposed autonomous system.
引用
收藏
页码：399 / 402
页数：4
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