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N2C2模式的研究

发布日期:2017-11-22   来源:《智能机器人》5期   作者: 李中年 侯威 王保国   浏览次数:1224
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【摘   要】:文中对应用于RBN(Reverse Baseline Networks)中的N2C2(Neural Network Controlled Communication)处理设施进行了研究。模拟结果表明,N2C2处理法优于传统的算法。

 关键字:N2C2RBN;阻塞;无冲突;能量函数

1 问题

众所周知,RBNReverse 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RBNN2C2Neural Network Controlled Communication)处理器,其中,N2Neural 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。

 

 
 
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