ID	原文	译文
8294	针对稀疏码分多址(sparse code multiple access,SCMA)采用消息传递算法(message passing algorithm,MPA)进行迭代解码中,功能节点(function node,FN)工作负担过重的问题,首先采用蒙特卡罗法得到了接收信号概率密度函数值在不同信噪比(signal-to-noise ratio,SNR)下的统计规律；	For sparse code division multiple access (sparse code multiple access, SCMA) USES the message passing algorithm (message passing algorithm, MPA) iterative decoding, utility nodes (function node, FN) the problem of overworked, first using the monte carlo method to get the received signal probability density function value in different SNR (signal - to - noise thewire, SNR) under statistical rule；
8295	然后针对接收信号概率密度函数值与外部信息值的关系,提出了基于门限判决减少FN负荷的部分外部信息传递的(partial extrinsic information transmission,PEIT)消息传递算法(PEIT-MPA)。	And then to receive signal probability density function value and the relationship between the external information value, was proposed based on threshold decision to reduce FN load part of the external information (partial extrinsic information transmission, PEIT) messaging (PEIT - MPA) algorithm.
8296	仿真结果表明,PEIT-MPA在几乎不改变系统误比特率和迭代收敛速度的条件下,降低了MPA复杂度,且SNR越高时,PEIT-MPA复杂度越低。	The simulation results show that PEIT - MPA in almost do not change the system bit error rate and the iterative convergence speed conditions, reduces the MPA complexity, and the higher SNR, PEIT - MPA complexity is lower.
8297	根据集对分析的基本原理,结合基于误差大小的集对分析组合预测,给出了基于误差方向的集对分析组合预测和基于模型性能的集对分析组合预测;	According to set pair analysis, the basic principle of combination of set pair analysis based on the error size combination forecast, based on the error is given in the direction of set pair analysis combination forecasting and the set pair analysis of combination forecast based on model performance;
8298	针对基于误差大小的集对分析组合预测建模过程中,从关联度的确定到组合权重的计算过程相对复杂、难以理解,推导简化了组合预测权重计算过程;	Based on error of the size of the set pair analysis in the process of the combination forecast model, from the correlation is sure to combination weights calculation process is relatively complex and difficult to understand, simplifies the combination forecast weight calculation process is derived;
8299	针对集对分析中同一度为0时导致组合预测模型信息丢失的问题,提出了一种基于折扣的同一度、差异度、对立度转换处理方法。	According to set pair analysis with once of 0 problem of information loss in combination forecast model, and puts forward a discount based on the antagonism with once, the difference degree, degree of conversion process.
8300	最后算例说明了该方法的有效性。	The final example illustrates the effectiveness of the method.
8301	针对网络中的非对称信息对抗及其纳什均衡解的问题,考虑了系统中的随机扰动,并采用累积费用的思想来确定信息对抗中的费用函数。	The asymmetric information in the fight against network and its Nash equilibrium problem, considering the random disturbance in the system, and USES the cumulative cost of ideas to determine information against the cost function.
8302	在所建立的信息对抗模型中,参与人包括不对等两方,即主导方和扰动方。	Information counter model are built, the participants including both sides, not the dominant party and disturbance.
8303	其中主导方的目标是最小化其费用函数,且最小化其方差,而扰动方的目标则只是最小化其费用函数。	The leading party's goal is to minimize the cost function, and minimize the variance, and disturbance of goal is minimizing the cost function.