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

页码：399 / 402

页数：4

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