1 December 1991 Noise tolerance of adaptive resonance theory neural network for binary pattern recognition
Author Affiliations +
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.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yong Soo Kim, Yong Soo Kim, Sunanda Mitra, Sunanda Mitra, "Noise tolerance of adaptive resonance theory neural network for binary pattern recognition", Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); doi: 10.1117/12.49787; https://doi.org/10.1117/12.49787


Pattern Recognition Using A Neural Network
Proceedings of SPIE (February 18 1988)
Spectral parameter estimation using neural network
Proceedings of SPIE (September 15 1992)
Neural Network Signal Processor (NSP)
Proceedings of SPIE (April 19 1988)
Novel RAM-based neural networks for object recognition
Proceedings of SPIE (October 30 1996)

Back to Top