自适应推理的知识求精

简要分析了传统的决策支持系统中的自适应知识库求精问题,提出了一种基于神经网络块间联系的规则的自适应推理知识求精算法.该算法能够有效地检查知识的一致性与相容性,及时发现并排除知识库知识之间的冗余和矛盾.该算法能够更好支持决策过程.     

Decentralized Cooperation Strategies in Two-Dimensional Traffic of Cellular Automata

We study the two-dimensional traffic of cellular automata using computer simulation. We propose two type of decentralized cooperation strategies, which are called stepping aside (CS-SA) and choosing alternative routes (CS-CAR) respectively. We introduce them into an existing two-dimensional cellular automata (CA) model. CS-SA is designed to prohibit a kind of ping-pong jump when two objects standing together try to move in opposite directions. CS-CAR is designed to change the solution of conflict in parallel update. CS-CAR encourages the objects involved in parallel conflicts choose their alternative routes instead of waiting. We also combine the two cooperation strategies (CS-SA-CAR) to test their combined effects. It is found that the system keeps on a partial jam phase with nonzero velocity and flow until the density reaches one. The ratios of the ping-pong jump and the waiting objects involved in conflict are decreased obviously, especially at the free phase. And the average flow is improved by the three cooperation strategies. Although the average travel time is lengthened a bit by CS-CAR, it is shorten by CS-SA and CS-SA-CAR. In addition, we discuss the advantage and applicability of decentralized cooperation modeling.     

基于批处理的远程证明机制

针对远程证明中存在的效率问题,在现有可信计算模块(TPM)的基础上,提出了批处理方案,将短时间段内到达的请求集中进行处理,并利用Merkle树进一步减少批处理方案中通信量增长的不足.在可信移动代理服务(MAS)中的应用表明,一定时间内到达的请求越多,基于批处理的远程证明的效率提高的程度越好.     

一种新的NBP网络增强组合算法

研究分析了已有NBP网络学习算法样本学习速度慢以及样本个体误差较大的原因,并在此基础上提出了一种基于训练集递减的自适应增强组合算法.通过对个体样本误差分布、泛化特性等方面的实例分析表明,该算法具有良好的泛化特性,既提高了样本学习速度,又改善了样本个体误差.