1 问题

众所周知，RBN（Reverse Baseline Networks）已广泛应用于：并行和分布式计算机网络、电话开关网络、综合服务数字网络等许多领域^[1][2][3]。本文研究涉及的是RBN的N²C²（Neural Network Controlled Communication）处理器，其中，N²（Neural Network）用来使函数的能量呈最大（在RBN约束条件下）。RBN的基本架构如图1所示，其中，开关盒不是位于直通状态就是位于交换状态。并且每个输入节点含有为开关配置的m个缓冲器。

3 结论

其一，在信息单元密度较高的情况下，N²C²方法的计算能力优于传统算法；其二，N²C²方法允许RBN的吞吐量出现最大；其三，N²C²法可有效解决许多相关的优化控制问题。

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作者简介

李中年 男（1949-）教授 研究方向:电力信息技术及智能控制。