目标函数

First, one. analyzes the eigenstates-from reachable sets and seeks the one which the target state belongs to. Then using the Grover iteration to amplify the probability amplitude of the desired eigenstate (the modul square of which is the probability of the corresponding eigenstate that the system will collapse to when it is measured). By measuring, the system will then collapse to the desired eigenstate with a probability of almost unity. Finally, one can use the admissible control to drive the system from the eigenstate to the target state.

该策略的核心思想是对本征态能控的系统，通过分析控制目标态与本征态的关系，寻找给定目标态所属的本征态可达集，然后利用量子非结构化搜索的Grover算法，将任意给定的系统初始态经过一定次数的Grover迭代，放大该本征态所对应的概率幅(概率幅的模方对应测量时波函数塌缩到对应本征态的概率)，然后对迭代后的态进行一次测量操作，使系统以接近1的概率塌缩到所需的本征态(前面分析得到的本征态可达集所对应的本征态)，最后用容许的控制将系统从该本征态控制到期望的目标态。

For non-Kolmogorov turbulence, the anisoplanatiam error, the angular correlation function of Zernike polynomials, the residual phase structure function and the long-exposure OTF after low-order modes correction are analyzed.

分析了非科尔莫戈罗夫湍流情况下，自适应光学系统低阶模式校正时的非等晕误差、泽尼克系数角相关函数、残余相位结构函数以及长曝光光学传递函数，并定义了一个新变量模式校正因子，用来有效评价目标非等晕误差对低阶模式校正自适应光学系统补偿效果的影响。

The primary work of this paper includes: 1 The general process and the fundamental theory of genetic algorithm is introduced systematically, which is also be applied to an example; 2 The penalty function method and the constraint tournament method are introduced in the paper, which are used to process the constraint limit. Via analyzing the basic principle of NSGA-II ,which is elaborated in the paper, the fast non- dominated sorting method, congestion distance and its comparison operator, the elite strategy and the main process of NSGA-II is researched particularly. 3 Via modeling the fire distribution problem, the model is computed by the algorithm of NSGA-II, which is combined with both the penalty function method and the restraint league tournament method The optimization of two goals are also realized in the paper. The results derived from the VC++6.0 indicate that, two algorithms both have the high convergence, simultaneously the Pareto optimal solution also maintain the variety of distribution.

本文所作的主要工作如下：系统地介绍了遗传算法的一般流程和基本理论，并应用于一个函数优化的实例；研究了用于处理约束限制的罚函数法和约束联赛法，论述了NSGA-II的基本原理，并对其提出的快速非支配排序法、拥挤度及其比较算子、精英策略及NSGA-II的主要流程作了详细的研究；建立了火力分配问题的模型，并将NSGA-II算法分别与罚函数法和约束联赛法相结合对该模型进行了计算，实现了对两个目标的最优化；用VC++6.0编程计算后所得的结果表明，两种算法均具有很好的收敛性，同时Pareto最优解也保持了好的多样性分布。

The magnetic field of submarine and the condition of depth charge engaging with submarine are deeply analyzed.

选用目标潜艇磁场强度的函数作为目标信号，深入分析了目标潜艇磁场以及深弹与目标潜艇的交会条件。

The experimental data were trained by BP neural network, and the results were curve fitted to set up a fitting function which was used in the genetic algorithm process to reach the optimum.

首先利用BP神经网络来训练已有实验值，然后将训练后的神经网络作为知识源，通过曲线拟合与逼近求得设计变量与目标函数值的函数关系表达式，最后将这一函数表达式作为遗传算法的适应度函数进行遗传迭代寻找最优解。

Furthermor, we consider their nondiferentiable situation, we define nonsmooth univex functions for Lipschitz functions by using Clarke generalized directional derivative and study nonsmooth multiobjective fractional programming with the new convexity. We establish generalized Karush-Kuhn-Tucker necessary and sufficient optimality condition and prove weak, strong and strict converse duality theorems for nonsmooth multiobjective fractional programming problems containing univex functions.

而且，本文利用Clarke广义方向导数针对Lipschitz函数在原来一致凸函数概念的基础上定义了不可微的一致凸函数，并利用这类新凸性，我们研究了非光滑多目标分式规划，获得了广义Karush-Kuhn-Tucher最优性条件；弱对偶定理、强对偶定理和严格逆对偶定理。

The analysis indicates that the mean intensity distribution on the receiver plane is closely related to the ratio of the lateral correlation length to the surface root mean square height.

研究了粗糙目标对相干光场的散射，导出了在接收平面上光场的自相关函数、光强度的分布和光强度的二阶矩，在分析粗糙目标对激光脉冲后向散射的基础上，提出了后向散射光强度与目标均方根高度和相关长度的关系，阐明了粗糙目标的后向散射机理。

Many polarimetric target decompositions were explored, and support vector machine was applied to the classification of polarimetric SAR image. A new algorithm of target classification was presented based on polarimetric target decomposition and support vector machine. Finally, the effect of classification results was discussed by selecting different kernels and different parameters.

五系统地对各种极化目标分解方法进行了深入的探讨，并应用支持向量机对极化SAR图像进行分类研究，提出了一种基于极化目标分解和支持向量机的目标分类算法，并通过选择各种类型的核函数以及不同的参数，探讨其对地物目标分类结果的影响。

Because constructing the fuzzy function is aim at every target of financial risk dividedly, the fuzzy function constructed has target and is a quantitative member belongs to [0,1]. Thus the constructing of function has more objective, and better reflect the unified target .

因为构造出的模糊函数是分别针对金融风险的每一个目标的，这样构造出的模糊函数带有目标性，且是[0，1]区间的一个量化数，这样函数的构造就比较有针对性，且更好地反映了目标化的统一。

Based on the above-mentioned parameter, according to the relationship between robust estimation and nonlinear diffusion, the Turkey loss function is introduced as the diffusion function in SAR image decomposition because of its better performance, and the contour of an image is extracted by the Turkey diffusion function with the proposed diffusion parameter mentioned above. The experiment results indicate that the Turkey loss function based diffusion process can strengthen the conspicuous contour. Additionally, a Raita\'s criterion-based method, solving the automatic diffusion threshold, is proposed to automatically set the threshold in diffusion decomposition.

针对基于梯度参数和局域方差系数的扩散在对SAR图像进行分解时存在的缺陷，提出了一种局域方差系数与窗口幅度均值积的扩散系数，该系数能够更有效地实现潜在目标区域和背景区域的区分；在此基础上，根据鲁棒估计与非线性扩散的联系，引入扩散效果更好的Turkey损失函数作为图像分解中的扩散函数，并结合局域方差系数与窗口幅度均值积的扩散参数来提取图像的轮廓，结果表明：基于Turkey损失函数的扩散过程对特征突出的边缘所起的&强化作用&更加明显；此外，针对扩散分解中的阈值确定问题，提出了一种基于拉依达准则的扩散阈值自动求解方法，实现了扩散阈值的自动求解。

However, as the name（read－only memory）implies, CD disks cannot be written onorchanged in any way.

然而，正如其名字所指出的那样，CD盘不能写，也不能用任何方式改变其内容。

Galvanizes steel pallet is mainly export which suits standard packing of European Union, the North America. galvanizes steel pallet is suitable to heavy rack. Pallet surface can design plate type, corrugated and the gap form, satisfies the different requirements.

镀锌钢托盘多用于出口，替代木托盘，免薰蒸，符合欧盟、北美各国对出口货物包装材料的法令要求；喷涂钢托盘适用于重载上货架之用，托盘表面根据需要制作成平板状、波纹状及间隔形式，满足不同的使用要求。