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

Yong Soo Kim; Sunanda Mitra

doi:10.1117/12.49787

1 December 1991 Noise tolerance of adaptive resonance theory neural network for binary pattern recognition

Yong Soo Kim, Sunanda Mitra

Proceedings Volume 1565, Adaptive Signal Processing; (1991) https://doi.org/10.1117/12.49787
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

Citation Download Citation

Yong Soo Kim and Sunanda Mitra "Noise tolerance of adaptive resonance theory neural network for binary pattern recognition", Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); https://doi.org/10.1117/12.49787

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