임계값 학습에 의한 Hopfield망의 기억 효율 개선

An Improvement of Memory Efficiency by Iearning Threshold on the Hopfield Network

In this paper, we proposed an algorithm to improve the memory efficiency by means of learning thresholds in spite of correlations among input patterns to be memorized. The proposed algorithm does not need preprocess correlations among input patterns but processes them with a threshold on a neural network. When memory contents are destroyed by correlation, nearly all patterns can be properly recovered with past learning. Through experiments we show how out algorithm can improve the memory efficiency.