一般多值双向联想记忆模型及其在IP地址识别中的应用

A General Model for the Multi-valued BAM and Its Applications in IP Address Recognition

通过引入模式相似度的概念,给出了一个一般的多值双向联想记忆模型.该模型囊括了Wang的指数式多值双向联想记忆(Mv-eBAM)以及多项式多值双向联想记忆(PBHC),并衍生出几种新的多值双向联想记忆模型,即正切联想记忆(HTBAM)和柯西联想记忆等.其中侧重对比讨论了HTBAM和Wang的PBHC模型,模拟结果显示HTBAM具有和PBHC相当的存储容量,且纠错性能显著提高.最后利用此性能,将HTBAM用于IP地址识别中,给出了一种新颖的联想IP路由查找方法.

A general model for multi-valued bi-directional associative memory (BAM) is presented. It is based on the concept of similarity measure. From the general model, we can derive Wang's multivalued exponential BAM (MV-eBAM), the polynomial bi-directional hetero-corrector (PBHC) and several new multi-valued BAM models, such as the hyperbolic tangent BAM (HTBAM) and Caushy BAM. Among these models, we place special emphasis on the comparisons between PBHC and HTBAM. Simulation results show that the proposed HTBAM model has a competitive storage capacity and much greater error-correcting capability than the PBHC model. Finally, we apply the HTBAM to IP address recognition and obtain a novel algorithm for associative IP routing lookups.