| 1. | Reliability evaluation of series system based on the fusion of kullback information
信息融合方法的串联系统可靠性评估 |
| 2. | Kullback ' s cross - entropy function is tried to smooth the minimal ncp - function and a non - interior continuation method is constructed for lcps
本章尝试用叉熵函数来光滑化极小化ncp函数。 |
| 3. | The relative ir radiation energy method and kullback divergence are used to evaluate ir reducing and ir countermeasures
分别用相对红外辐射能量法和库尔巴克散度对红外信号减缩措施和红外对抗措施进行了评估。 |
| 4. | Shannon entropy function is used to smoothing it . a non - interior predictor - corrector continuation method is built for a class of mixed complementarity problems
基于非线性互补问题的一个等价不动点格式，第五章分别用shannon熵函数和kullback熵函数光滑化max函数。 |
| 5. | Its global convergence is analyzed . shannon entropy and kullback ' s cross - entropy are used to smooth the max function respectively in a fixed - point formulation of ncps in chap
虽然fang - puthenpure 、 tuncel - todd 、 wright等都认识到这个问题的重要性，但目前这方面的工大连理工大学博士论文作还很少。 |
| 6. | Enlightened by the idea of aic , this paper treats the data of the same group as samples of certain distribution . in this way , it determines the number of groups by seeking the asymptotically unbiased estimate of kullback - leibler information
在aic准则思想的启发下，将应该同属于一个分类的数据看作是在某一分布中抽取的样本，从而通过求kullback - leibler信息量的渐近无偏估计而达到确定类数与数据分类的目的。 |
| 7. | The applications of shannon entropy and kullback ' s cross - entropy as perturbations are discussed . by maximizing the perturbed lagrangians in dual space , we obtain the exponential penalty function and exponential multiplier penalty function , respectively , for inequality constrained nlps
文中分别以shannon熵函数和kullback叉熵函数作为摄动函数，导出其对偶函数分别是原问题的指数罚函数和指数乘子罚函数。 |
| 8. | We evaluate the statistical visibility of information hiding algorithms based on the coefficient of kurtosis , differential entropy , goodness of fit test and kullback entropy . on these bases some methodologies are suggested to defend against statistical steganalysis
重点研究了含密序列统计可见性的定量评估，提出了基于峰度系数和微分墒的偏态评估、基于拟和优度检验的统计可见性评估和基于鉴别信息的统计可见性评估。 |
| 9. | From the unique viewpoint of shannon information theory , this dissertation investigates the varying and transmission of information and uncertainty in control systems using measures of entropy , mutual information , kullback - leibler information and information rates , in time and frequency domains , respectively . several problems concerning state estimation , modeling , h control , performance limits and design constraints of control systems are addressed under this framework
论文以有别于传统控制理论的独特视角? ?信息的观点研究控制系统，采用shannon信息论中的熵、互信息、 kullback - leibler信息及相应的信息率分别考察了控制系统在时域和频域中信息和不确定性的传输和变化，讨论了状态估计、模型化、 h _控制及控制系统性能极限和设计约束等领域的相关问题。 |
| 10. | 2 ) by analyzing the information and conditional information description mechanism of system states , the problem of stochastic model reduction is investigated based on state aggregation . the information loss and conditional information loss between the full - and reduced - order models are measured by entropy , while the independence and conditional independence within me components of aggregated state are measured by kullback - leibler information distance . several model reduction methods for stable and unstable linear systems are derived by employing two criteria to get aggregation matrices : the minimal information loss and the maximal independence
2 )分析了随机系统状态空间模型中的信息和条件信息描述机制，以shannon熵为手段描述线性系统模型降阶过程中的信息和条件信息损失，以kullback - leibler信息作为衡量降阶模型状态向量各分量之间统计独立性的测度，针对稳定和不稳定系统研究基于状态集聚的模型降阶问题：分别运用最小信息损失准则和最大独立性原则，得出几种状态集聚的信息论方法，并讨论降阶模型的性质、阶次的确定、系统噪声分布特性等问题。 |
