ID 原文 译文
10154 解决这一问题,可以采用机器学习的方法基于战场辅助决策系统的武器-目标分配,从已知的决策中推理生成出新的决策,而不必每个步骤中都重新搜索新的目标分配方案。 Can solve this problem, using machine learning method based on the battlefield auxiliary decision system of weapon target assignment, to generate a new decision from the known decision reasoning, rather than every step to search the new target allocation.
10155 根据这种思路,提出了一种基于类型2区间模糊K近邻分类器的武器-目标分配方法,利用分支定界法得到的分配方案作为训练样本,通过构造并行运行的类型2区间模糊K近邻分类器来推导目标分配结论,实现了快速决策的目的。 According to this thinking, this paper proposes a fuzzy K nearest neighbor classifier based on type 2 range weapons - target allocation method, the distribution of the branch and bound method are used to get the solution as the training sample, by constructing parallel type 2 interval fuzzy K neighbor classifier to target distribution is derived conclusions, realized the purpose of quick decision.
10156 由于受到模糊集理论的限制,模糊时间序列预测理论在不确定数据集的描述上有失客观,针对这种局限性,提出一种直觉模糊时间序列预测模型。 Due to the limitation of the fuzzy set theory, the theory of fuzzy time series prediction on the description of the uncertain data set is objective, according to this limitation, a kind of intuitionistic fuzzy time series forecasting model is put forward.
10157 应用模糊聚类算法实现论域的非等分划分;‭ ‭Applying the theory of fuzzy clustering algorithm implementation of equal division;
10158 针对直觉模糊时间序列的数据特性,提出一种更具客观性的隶属度和非隶属度函数的确定方法; ‭Based on the data characteristics of intuitionistic fuzzy time series, this paper puts forward a more objective method for determining the relative membership degree and the membership functions.
10159 ‭提出一种基于直觉模糊近似推理的模型预测规则。 ‭In this paper, a model prediction rules based on intuitionistic fuzzy approximate reasoning.
10160 在Alabama大学入学人数和中国社会消费品零售总额数据集两组数据集上分别与典型方法进行对比实验,结果表明该模型有效提高了预测精度,证明了模型的有效性和优越性。 ‭Enrollment at the university of Alabama and China's total retail sales of social consumer goods data set two groups of data sets, respectively compared with typical method experiment, the results show that the model improves the prediction accuracy effectively, to prove the validity and superiority of the model.
10161 传统的基于最优成像空间的双基地合成孔径雷达(synthetic aperture radar,SAR)成像算法是一种性能卓越的SAR成像算法,其具有散射点在成像空间定位准确,算法复杂度低以及聚焦性能高等优点。 Traditional based on the optimal imaging space of bistatic synthetic aperture radar (synthetic aperture radar, SAR) imaging algorithm is a kind of the excellent performance of SAR imaging algorithm, its scattering point in accurate imaging space, low algorithm complexity and high focus on performance, etc.
10162 然而,随着观测场景的扩大,该算法会出现散射点在成像空间定位不准确以及聚焦性能下降的缺点。 However, with the enlargement of the observation scene, the algorithm will be scattered points in the image space positioning inaccurate and focusing on the disadvantage of performance degradation.
10163 为了克服这些缺点,在SAR成像领域首次提出了距离历史向量匹配比的概念,基于该概念,又进一步提出了一种新的基于广义最优成像空间的双基地SAR成像算法。 In order to overcome these shortcomings, is proposed for the first time in the field of SAR imaging distance vector matching than the concept of history, based on this concept, and further puts forward a new kind of imaging based on generalized optimal space of bistatic SAR imaging algorithm.