Hely T A, Willshaw D J, Hayes G M
Centre for Cognitive Sci., Edinburgh Univ.
IEEE Trans Neural Netw. 1997;8(3):791-4. doi: 10.1109/72.572115.
The sparse distributed memory (SDM) was originally developed to tackle the problem of storing large binary data patterns. The model succeeded well in storing random input data. However, its efficiency, particularly in handling nonrandom data, was poor. In its original form it is a static and inflexible system. Most of the recent work on the SDM has concentrated on improving the efficiency of a modified form of the SDM which treats the memory as a single-layer neural network. This paper introduces an alternative SDM, the SDM signal model which retains the essential characteristics of the original SDM, while providing the memory with a greater scope for plasticity and self-evolution. By removing many of the problematic features of the original SDM the new model is not as dependent upon a priori input values. This gives it an increased robustness to learn either random or correlated input patterns. The improvements in this new SDM signal model should be also of benefit to modified SDM neural network models.
稀疏分布式内存（SDM）最初是为解决存储大型二进制数据模式的问题而开发的。该模型在存储随机输入数据方面取得了很好的成功。然而，它的效率，特别是在处理非随机数据时，很差。以其原始形式，它是一个静态且不灵活的系统。最近关于SDM的大部分工作都集中在提高一种将内存视为单层神经网络的修改形式的SDM的效率上。本文介绍了一种替代的SDM，即SDM信号模型，它保留了原始SDM的基本特征，同时为内存提供了更大的可塑性和自我进化空间。通过去除原始SDM的许多问题特征，新模型对先验输入值的依赖程度较低。这使其在学习随机或相关输入模式时具有更高的鲁棒性。这种新的SDM信号模型的改进也应该对修改后的SDM神经网络模型有益。
