自动化学报
自動化學報
자동화학보
ACTA AUTOMATICA SINICA
2005年
5期
668-674
,共7页
杜升之%陈增强%袁著祉%张兴会
杜升之%陳增彊%袁著祉%張興會
두승지%진증강%원저지%장흥회
Associative memory%neural network%noise sensitivity%digital pattern
This paper analyzes noise sensitivity of bidirectional association memory (BAM) and shows that the anti-noise capability of BAM relates not only to the minimum absolute value of net inputs(MAV), as some researchers found, but also to the variance of weights associated with synapse connections. In fact, it is determined by the quotient of these two factors. On this base, a novel learning algorithm-small variance leaning for BAM(SVBAM) is proposed, which is to decrease the variance of the weights of synapse matrix. Simulation experiments show that the algorithm can decrease the variance of weights efficiently, therefore, noise immunity of BAM is improved. At the same time, perfect recall of all training pattern pairs still can be guaranteed by the algorithm.
