ID 原文 译文
58768 然后,将该序列作为滑动自适应三次指数平滑法的输入,并生成安全态势初始预测值序列; Then, the sequences are taken as the input of the sliding adaptive cubic exponential smoothing method with initial security situation predicted value sequences generated.
58769 最后,基于误差状态通过时变加权马尔科夫链预测误差,并修正初始预测值。 Finally, the time-varying weighted Markov chain is used to predict the error value based on the error state and the initial predicted values are modified.
58770 实验结果表明, 自适应预测模型相比其他模型具有较好的预测精度。 Experimental results show that the NAP has a better prediction accuracy than other existing models.
58771 由于多进多出雷达的机载平台运动,导致机载多进多出雷达的杂波是非高斯的并且是非均匀的,无法获得独立同分布的训练数据来估计杂波的协方差矩阵。 Due to the moving platforms, the clutters in distributed airborne MIMO radar are non-Gaussian and non-homogeneous, which leads to having no independent and identically distributed training data to estimate the clutter covariance matrix.
58772 为解决这一问题,提出把非高斯杂波的协方差建模为未知随机的且服从逆复威沙特分布的随机过程,其均值建模为杂波协方差矩阵的锐化因子和杂波多普勒谱的阿达马乘积。 To solve the problem, we propose that the covariance of the clutter should be modeled as an inverse complex Wishart distribution whose average value is a Hadamard product of the covariance matrix taper (CMT) and the clutter Doppler spectrum component.
58773 在此基础上采用贝叶斯方法和广义似然比检验准则,设计了一种新型的自适应检测器。 Based on this clutter model, a novel detector combing the Bayesian approach and the generalized likelihood ratio test(GLRT) is proposed.
58774 数值仿真结果表明:所设计的检测器的性能要好于目前常用的两种非贝叶斯类检测器。 Numerical simulation results show that the proposed detector has a better detection performance compared with two current commonly used non-Bayesian detectors.
58775 针对现有的基于隐马尔可夫模型的边缘节点状态预估算法存在参数初值选取主观性较强、特征权重设置依赖经验、多维特征节点分析适应性差等问题,提出一种改进的边缘层节点健康状态预估算法。 An improved state prediction algorithm for edge layer nodes is proposed to solve the problem of the existing state prediction algorithm for edge layer nodes based on Hidden Markov, such as the subjectivity of initial parameter selection, the dependence of feature weights setting on experience, and the bad adaptability of multidimension feature node analysis.
58776 首先在算法的数据处理层,基于聚类实现对模型参数和观测序列量化进行优化;然后在算法的训练层,用多个单特征隐马尔可夫过程对多特征隐马尔可夫模型进行建模;最后在算法优化层,采用基于信息增益的自适应遗传算法对隐马尔可夫模型生成的状态序列进行优化和约简,有效地解决了特征权重设置和参数初值选取主观性强的问题。 At the data processing layer of the algorithm, the parameter of the model and observation sequence are optimized by the method of clustering; and then at the training layer of the algorithm, the single-feature Hidden Markov Model is used to model the multi-feature Hidden Markov Model; finally, an adaptive genetic algorithm based on the information gain is used to optimize and reduce the state sequence generated by the Hidden Markov Model. The problems of feature weight setting and parameter initial value selection are solved effectively.
58777 实验结果表明,与现有算法比较,该算法有效地提高了大规模边缘层节点的高维度健康状态的准确性。 Experimental results show that the proposed algorithm effectively improves the accuracy of the high-dimensional health state of large-scale edge layer nodes compared with the existing algorithms.