摘要
In this paper we propose a new discrete bidirectional associative memory (DBAM) which is derived from our previous continuous linear bidirectional associative memory (LBAM). The DBAM performs bidirectionally the optimal associative mapping proposed by Kohonen. Like LBAM and NBAM proposed by one of the present authors,the present BAM ensures the guaranteed recall of all stored patterns,and possesses far higher capacity compared with other existing BAMs,and like NBAM, has the strong ability to suppress the noise occurring in the output patterns and therefore reduce largely the spurious patterns. The derivation of DBAM is given and the stability of DBAM is proved. We also derive a learning algorithm for DBAM,which has iterative form and make the network learn new patterns easily. Compared with NBAM the present BAM can be easily implemented by software.
提出了一种基于连续的线性双向联想记忆（LBAM）的离散双向联想记忆（DBAM）．DBAM双向地进行K。honen提出的最优联想映射．同作者已提出的LBAM和NBAM一样，DBAM可保证对所有已存储模式的回忆，并具有较现有其它BAM高得多的容量，还和NBAM一样具有对输出模式中噪声的强抑制能力，并因此大大减少了伪记忆．给出了对DBAM的推导并证明了其稳定性．还推导了DBAM的学习算法，该算法具有选代形式，使网络易于学习新模式同NBAM相比，DBAM易于用软件实现．
