基于径向基函数网络或模糊系统的非线性连续时间系统的自适应调节

对于一类连续时间的非线性动态系统x=f(x)+Bu+d,当系统中的非线性函数f(x)满足有界或线性增长条件(具有未知的增长系数)时,首先证明了f(x)中的x落入一紧集中,然后根据径向基函数网络或模糊系统的逼近性质,给出了两种自适应调节器的设计方法.利用李亚普诺夫稳定性理论,证明了控制算法是全局稳定的,闭环系统的状态是一致最终有界的,且控制律是稳定的.     

基于深度贝叶斯网络学习的不确定性建模方法

战场态势评估涉及很多不确定因素,对不确定性进行仿真建模能够提高态势评估的能力。针对参战对象多元、不确定性增多导致的无法全面准确表达不确定性问题,提出了基于记忆模块和变分自编码器的深度贝叶斯网络模型。采用生成模型设计了基于深度贝叶斯网络学习的态势评估模型;阐述了融合记忆模块的深度生成模型原理和模型的学习与推理过程;以某空袭行动为例构建贝叶斯网络,对所提方法进行了验证。结果表明:深度神经网络能够逼近隐变量的非线性变换,设计的记忆模块能存储深度神经网络提取的大量局部特征,通过学习自动得到了贝叶斯网络条件概率,增强了不确定性建模能力。     

置信水平过渡中应注意的问题

在置信水平过渡中,常采取在合成不确定度σ的基础上,乘以置信因子C的方法,这种方法有时会造成置信水平失实,分析失实的原因和应注意的问题.     

多面体不确定线性系统的鲁棒D-稳定研究

针对一类多面体不确定线性系统给出一种新的鲁棒D-稳定的充分条件,该条件可以由一族线性矩阵不等式(LM Is)的可行解得到,而这族LM Is可以由多面体的顶点描述。通过引入参数相关Lyapunov函数使得该条件的保守性降低,与其他条件相比这个条件具有更广泛的适用性,推广并改进了现有成果。     

对测不准关系的认同与争议

介绍了测不准关系的一些不同的表述和证明方法,对其中关于这一原理的认同和有争议的问题进行了比较与分析.结合殿村和蔡林格(Zeilinger)近期完成的电子和中子衍射实验的结果,评述了有关量子理论的新进展.这些实验充分地证明了单个粒子具有波动性的事实.测不准关系原本是微观粒子波动性质的必然结果,所以它应当适用于单个微观粒子.     

不确定离散脉冲系统的保成本控制研究

许多实际系统都可归结为基于脉冲差分方程数学模型所描述的离散脉冲系统,针对此类离散脉冲系统,考虑一类范数有界时变参数不确定性和一个二次型性能指标,研究了其保成本状态反馈控制问题.首先根据李亚普诺夫稳定性理论与鲁棒控制的基本原理,给出了存在保成本控制器的一个充分条件,然后依据范数有界性参数不确定性已有的结论证明了该条件等价于一个线性矩阵不等式的可解性问题,并用这组线性矩阵不等式的可行解给出了保成本控制律的一个参数化表示.     

基于线性矩阵不等式的贷款组合鲁棒优化模型

运用线性矩阵不等式方法,研究了商业银行贷款组合选择的鲁棒优化问题.在Markowitz均值-方差理论基础上,建立了贷款组合鲁棒优化模型,并用多个期望收益向量和协方差矩阵描述未来贷款收益的不确定性,给出了线性矩阵不等式的求解方法.用数值仿真验证了模型的有效性.由于模型考虑了未来贷款收益的不确定性,得到了可靠性较高的结果.研究结果表明,该模型具有鲁棒性,可以有效降低贷款风险,并可为商业银行提供贷款决策依据.     

双时相影像联合不确定性对变化检测精度的影响机理探索

研究了双时相影像的联合不确定性对变化检测结果精度的影响机理,为通过抑制不确定性的方法提高变化检测精度的工作奠定理论基础.首先利用联合熵对双时相影像的联合不确定性进行量化评估;进而基于空间统计相关方法,研究影像的联合不确定性与双时相影像变化检测精度指标之间的关系;最后建立双时相影像联合不确定性对变化检测结果精度的作用模型.实验结果表明：双时相影像的联合不确定性与变化检测结果的精度之间呈现出强负相关性,且对变化检测结果精度的影响模式具有线性特征.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.