Learning, adaptation and evolution for intelligent system

Author

Fukuda, Toshio

Author_Institution

Dept. of Mechano-Inf. & Syst., Nagoya Univ., Japan

Volume

1

fYear

1997

fDate

7-11 Jul 1997

Abstract

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 provides 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

Keywords

control system synthesis; fuzzy control; genetic algorithms; hierarchical systems; intelligent control; learning (artificial intelligence); neurocontrollers; optimal control; robots; adaptation; automation systems; control design optimization; evolution; fuzzy systems; genetic algorithm; hierarchical robotic system; intelligent control systems; learning; neural network; scheduling problems; software development; system complexities; Artificial intelligence; Biological neural networks; Fuzzy systems; Genetic algorithms; Intelligent robots; Intelligent sensors; Intelligent structures; Intelligent systems; Knowledge representation; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, 1997. ISIE '97., Proceedings of the IEEE International Symposium on

Conference_Location

Guimaraes

Print_ISBN

0-7803-3936-3

Type

conf

DOI

10.1109/ISIE.1997.651718

Filename

651718