An ART2-TPM neural network for automatic pattern classification

Author

Fujita, Masahiro ; Bavarian, Behnam

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA

fYear

1991

fDate

8-14 Jul 1991

Firstpage

479

Abstract

Presents a novel two-layer neural network based on the adaptive resonance theory (ART2) network for continuous variables in which the bottom-up long-term memory (LTM) and the top-down LTM adaptations use the self-organizing topology preserving mapping (TPM) learning rule. This topology is developed in the context of an extended Neocognitron for automatic pattern clustering from raw two-dimensional image inputs to a finite number of classes. The complete ART2-TPM algorithm is presented and an illustrative example for automatic recognition of line orientation is given. The performance of the network is compared to that of the winner-take-all network

Keywords

adaptive systems; computerised pattern recognition; learning systems; neural nets; resonance; self-adjusting systems; topology; ART2-TPM neural network; Neocognitron; adaptive resonance theory; automatic pattern classification; learning rule; line orientation; long-term memory; pattern clustering; performance; self-organizing topology preserving mapping; two-dimensional image inputs; two-layer neural network; winner-take-all network; Clustering algorithms; Computer vision; Network topology; Neural networks; Neurons; Pattern classification; Pattern clustering; Pattern recognition; Resonance; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7803-0164-1

Type

conf

DOI

10.1109/IJCNN.1991.155380

Filename

155380