Incorporating model uncertainty into optimal insurance contract design

In stochastic optimization models, the optimal solution heavily depends on the selected probability model for the scenarios. However, the scenario models are typically chosen on the basis of statistical estimates and are therefore subject to model error. We demonstrate here how the model uncertainty can be incorporated into the decision making process. We use a nonparametric approach for quantifying the model uncertainty and a minimax setup to find model-robust solutions. The method is illustrated by a risk management problem involving the optimal design of an insurance contract.     

非惯性系下柔性悬臂梁的振动主动控制

采用变结构控制方法对非惯性系下柔性悬臂梁的振动主动控制进行研究．重点通过算例揭示一次近似模型与传统的零次近似模型的巨大差异，以及变结构方法在控制非惯性系下柔性悬臂梁的稳态振动的有效性．结果表明，当大范围旋转运动角速度较大时，传统零次近似模型不能对动力系统进行正确的数学描述；变结构控制方法能够使得非惯性系下梁的稳态振动得到完全镇定，且该方法对转动角速度变化具有较好的鲁棒性；采用零次近似模型进行控制设计的控制效果将在某一临界角速度条件下出现失效，该临界角速度值大于静止悬臂梁的基频．     

基于核各向异性扩散的红外小目标检测

为了减少红外图像中背景边缘对检测的影响,提出了一种具有鲁棒性的弱小目标检测算法,该算法利用核各向异性扩散模型进行背景预测,再与原图像差分实现弱小目标检测。为了提高算法的自适应能力,提出了一种鲁棒性扩散系数,能够根据图像背景的起伏程度自适应调整扩散系数曲线的陡峭程度。实验结果表明,与现有的检测算法相比,该算法能够在不同类型的复杂背景下有效抑制背景及其边缘,保留目标大小,降低虚警率,具有更强的鲁棒性。     

基于自适应BP神经网络的结构损伤检测

描述基于人工神经网络的结构损伤检测的基本步骤以及该方法在实际5层钢框架结构损伤检测上的应用．提出了一种改进的BP神经网络方法，它能够解决传统BP神经网络在实际应用中存在的两个问题：收敛速度慢并存在局部极小．其基本思想是引入动态自适应算子加速传统BP算法的梯度下降速度，从而提高运算速度，通过自调节保证学习过程中每一时刻具有较大的sigmoid函数值，从而可以避免局部极小．数值仿真结果表明基于该自适应神经网络的结构损伤检测方法具有强的鲁棒性，而且与传统的BP神经网络相比，不仅提高了计算速度，并且具有很高的精度．最后，实例的应用也证明了该方法的有效性．     

耦合时空混沌的模糊混沌神经网络鲁棒自适应控制

将模糊逻辑系统和混沌神经网络结合起来,利用模糊逻辑系统的逼近能力和混沌神经网络的时空混沌行为,对模型未知的耦合时空混沌系统提出了一种模糊混沌神经网络自适应控制方案;同时考虑系统扰动、未建模动态特性和建模误差的影响,设计自适应补偿器,增强时空混沌系统控制的鲁棒性;并用Laypunov方法证明了该方案的稳定性;仿真验证了方案的有效性和鲁棒性。     

Robustness in deterministic multi-objective linear programming with respect to the relative interior and angle deviation

This paper deals with the robustness issue in deterministic multi-objective linear programming from two new standpoints. It is shown that a robustness notion recently reported in the literature is equivalent to strict efficiency. Corresponding to an efficient solution, a new quantity, robustness order (RO) is defined with respect to the interiority order of the cost matrix in the binding cone. A linear programming problem is provided to calculate the RO of a given efficient solution. The second part of the paper is devoted to investigating the robustness with respect to the eligible angle deviation of the cost matrix in the binding cone. Theoretical results are given to obtain the maximum eligible angle deviation. Finally, the relationship between two above-mentioned robustness standpoints is established. To have a better geometrical view, we prove the results for single-objective LP problems at first, and then we extend them to the multi-objective case. In addition to the theoretical results, some clarifying examples are given.     

Bayesian robustness under a skew-normal class of prior distribution

We develop a global sensitivity analysis to measure the robustness of the Bayesian estimators with respect to a class of prior distributions. This class arises when we consider multiplicative contamination of a base prior distribution. A similar structure was presented by van der Linde [12]. Some particular specifications for this multiplicative contamination class coincide with well known families of skewed distributions. In this paper, we explore the skew-normal multiplicative contamination class for the prior distribution of the location parameter of a normal model. Results of a Bayesian conjugation and expressions for some measures of distance between posterior means and posterior variance are obtained. We also elaborate on the behavior of the posterior means and of the posterior variances through a simulation study.     

不确定混沌系统的混合投影同步

本文研究了一类不确定混沌(超混沌)系统的混合投影问题.利用自适应方法和Lyapunov稳定性理论,获得了两个恒同或不同混沌系统实现混沌投影同步的一般方法.最后,数值仿真的结果验证了方法的有效性和鲁棒性.     

A high‐order element based adaptive mesh refinement strategy for three‐dimensional unstructured grid

Adaptive mesh refinement (AMR) shows attractive properties in automatically refining the flow region of interest, and with AMR, better prediction can be obtained with much less labor work and cost compared to manually remeshing or the global mesh refinement. Cartesian AMR is well established; however, AMR on hybrid unstructured mesh, which is heavily used in the high‐Reynolds number flow simulation, is less matured and existing methods may result in degraded mesh quality, which mostly happens in the boundary layer or near the sharp geometric features. User intervention or additional constraints, such as freezing all boundary layer elements or refining the whole boundary layer, are required to assist the refinement process. In this work, a novel AMR strategy is developed to handle existing difficulties. In the new method, high‐order unstructured elements are first generated based on the baseline mesh; then the refinement is conducted in the parametric space; at last, the mesh suitable for the solver is output. Generating refined elements in the parametric space with high‐order elements is the key of this method and this helps to guarantee both the accuracy and robustness. With the current method, 3‐dimensional hybrid unstructured mesh of huge size and complex geometry can be automatically refined, without user intervention nor additional constraints. With test cases including the 2‐dimensional airfoil and 3‐dimensional full aircraft, the current AMR method proves to be accurate, simple, and robust.