Learning, adaptation and evolution for intelligent system

被引：0

作者：

Fukuda, T ^{[1
]}

机构：

[1] Nagoya Univ, Dept Micro Syst Engn, Chikusa Ku, Nagoya, Aichi 46401, Japan

来源：

ISIE '97 - PROCEEDINGS OF THE IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS, VOLS 1-3 | 1997年

关键词：

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：

There are many growing demands for making systems intelligent, by which people can cope with the system complexities and software development. The intelligent system must have the capabilities, in principle, for learning, adaptation and evolution, so that the system can adapt to the change of environments, tasks, and systems themselves. This paper will provide the foundation and methodologies for the learning, adaptation and evolution, by neural network, fuzzy system and genetic algorithm. Those methods can be applied for various optimization of design, and scheduling problems in automation systems.

引用

页码：TU9 / TU14

页数：6

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