The kanerva ' s sparse distributed memory ( sdm ) tackles the problem of training large data patterns and extendes the storage mode of existing computer . but it ' s address array produced randomly ca n ' t reveal the distribution of patterns and it has ' t the ability of function approximation for its learning rule Kanerva的稀疏分布存储( sdm )模型解决了大维数样本的训练问题，推广了现有计算机的存储方式。但其地址矩阵的随机预置方式不能反映样本的分布，并且sdm的学习方式使之不能用于函数逼近及时间序列预测问题。
