Noise tolerance of adaptive resonance theory neural network for binary pattern recognition

Yong Soo Kim, Sunanda Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Assuming a fast learning condition for an adaptive resonance theory (ART) type neural network, we have explored the effect of the vigilance parameter and the order function on the performance of the neural network for binary pattern recognition. A modified search order was developed for classification of binary alphabet characters and airplane classes and compared with the performance of the original ART-1 network for binary pattern recognition with and without the presence of noise. Our results suggest that the effect of noise on binary pattern recognition is solely dependent on the induced changes in the critical feature patterns when other control parameters remained the same.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsSimon Haykin
PublisherPubl by Int Soc for Optical Engineering
Pages323-330
Number of pages8
ISBN (Print)0819406937
StatePublished - 1991
EventAdaptive Signal Processing - San Diego, CA, USA
Duration: Jul 22 1991Jul 24 1991

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume1565
ISSN (Print)0277-786X

Conference

ConferenceAdaptive Signal Processing
CitySan Diego, CA, USA
Period07/22/9107/24/91

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