An adaptive recognition using self-organized network

Author

Miyanaga, Yoshikazu ; Tochinai, Koji

Author_Institution

Dept. of Electron. Eng., Hokkaido Univ., Sapporo, Japan

Volume

1

fYear

1992

fDate

10-13 May 1992

Firstpage

332

Abstract

An adaptive recognition system that is based on self-organization is proposed. The method estimates the cluster distribution of given data and recognizes an unknown input datum at the same time. The clustering/recognizing of a given characteristic vector is based on the Mahalanobis distance. By using adaptation, it is possible to reconstruct the cluster set suitable for the given characteristic data even if the distribution of these data changes with time. It is also shown that the total number of nodes can be minimized by using the rules of node merging. The adaptability and the generalizability of the clustering and recognition are explored

Keywords

learning systems; neural nets; pattern recognition; self-adjusting systems; Mahalanobis distance; adaptive recognition; characteristic vector; cluster distribution; node merging; self-organized network; Adaptive systems; Algorithm design and analysis; Character recognition; Clustering algorithms; IEEE members; Merging; Simultaneous localization and mapping; Supervised learning; Time varying systems; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0593-0

Type

conf

DOI

10.1109/ISCAS.1992.229946

Filename

229946