Self-organizing model of learning and its application to eyesight system of robot

Author

Suzuki, Hisashi ; Arimoto, Sugurn

Author_Institution

Fac. of Eng. Sci., Osaka Univ., Japan

fYear

1988

fDate

25-27 May 1988

Firstpage

411

Lastpage

416

Abstract

A self-organizing model that dynamically constructs balanced trees of a database to universally learn any relation between input signals and output signals of unknown systems, is proposed. Contrary to conventional databases that memorize data individually, the self-organizing model forms an associative memory which realizes the function of learning. Applications of the model to position identification and obstacle avoidance in an autonomous mobile robot system, as well as to continuous handwritten letter recognition are shown, together with some successful experimental results.<>

Keywords

computerised pattern recognition; content-addressable storage; database management systems; learning systems; mobile robots; self-adjusting systems; associative memory; autonomous mobile robot system; balanced trees; continuous handwritten letter recognition; database; experimental results; eyesight system; input signals; learning model; obstacle avoidance; output signals; position identification; self-organizing model; unknown systems; Acceleration; Associative memory; Data engineering; Databases; Equations; Handwriting recognition; Machine learning; Mobile robots; Neural networks; Terminology;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence for Industrial Applications, 1988. IEEE AI '88., Proceedings of the International Workshop on

Conference_Location

Hitachi City, Japan

Type

conf

DOI

10.1109/AIIA.1988.13325

Filename

13325