一种基于RCAM结构的联想记忆器

A Novel Associative Memory Based on the Architecture of Recurrent Correlation Associative Memory

递归相关联想记忆（ＲＣＡＭ）的回忆规则不同于Ｈｏｐｆｉｅｌｄ网络之处在于前者在输入与记忆模式的相关值上作用一非线性函数．在文献［７］的基础上，文中对所涉及的非线性函数进行了进一步的研究，提出了利用截断较小相关值来提高记忆性能的方法，得到了一种新的具有ＲＣＡＭ结构的联想记忆器（ＴＲＣＡＭ）．理论分析表明该方法可大大地提高记忆器对任意输入的信噪比，仿真实验也显示此方法可显著增大记忆模型在保证一定纠错能力下的记忆容量．

The main difference between the recall rule of recurrent correlation associative memory and that of Hopfield network is that RCAM operates a nonlinearity upon the correlations between input and stored patterns. In this paper, it proposes to improve the performance of RCAM by truncating the insignificant correlations, produce a novel associative memory based on the architecture of RCAM, noted as TRCAM. The theoretical analysis shows that the method raises the signal to noise ratio greatly. The simulations illustrate that this model enjoys a high capacity