面向问题的稀疏分布式记忆模型

 在Kanerva所提出的稀疏分布式记忆(SDM)或存贮模型的基础上,为实现对特定类问题的大维数输入空间的模式识别,如汉字识别,脸谱辩认等,根据问题的具体情况,诸如汉字的频率分布等,提出了一个面向问题的稀疏分布式记忆模型。改进后的模型更符合实际应用,其中的学习规则采用了指数型记忆规则,使模型具有更高的信噪比,存贮容量亦大大提高。计算机模拟表明了这一点。

ORIENTED-PROBLEM SDM MODEL

Based on Kanerva's Sparse Distributed Memory Model (SDM), in order to recognize patterns such as Chinese character recognition and face identification problems in a large dimensional input space, an oriented-problem SDM is proposed according to such concrete cases as the frequency distribution of Chinese characters and the characteristic distributions of faces, so that the model is more practical. The Hebb learning rule in SDM is replaced by the exponential learning rule. In terms of our theoretical analysis, the signal noise ratio and memory capacity of the modified model are obviously improved and the ability of SDM is extended. The simulation of computer shows its agreement with the above analysis.