本文旨在应用随机最优化原理，建立求解这一复杂问题的有效的系统工程方法。
The work presented in this thesis aims at creating a systematic and stable method based on stochastic optimization strategy to tackle this problem.
分析了随机最优化领域中广义矩问题及其对偶问题的关系，从而提出了广义矩问题的一种新的求解方法。
Analyzes the relation between the generalized moment problem and its dual problem, and gives a new method for solving some generalized moment problems.
传统的最优化技术大多是基于梯度寻优技术或随机搜索的方法。
Traditional optimization techniques search for the best solutions using gradients or random searching.
进化规划(EP)是一种随机优化技术，它可以发现全局最优解。
Evolutionary Programming (EP), a multi agent stochastic optimization technique, can lead to global optimal solutions for complex problems.
标准的粒子群优化算法作为一种随机全局搜索算法，因其在种群中传播速度过快，易陷入局部最优解。
The standard particle swarm optimization algorithm as a random global search algorithm, because of its rapid propagation in populations, easily into the local optimal solution.
该算法本质上是一种随机搜索算法，并能以较大概率收敛到全局最优，特别适用于连续函数的优化。
The algorithm is a random searching algorithm in nature. It can converge to the global minima more probability and be adept in continuous functions optimization.
将基于直接搜索法的随机全局优化方法用于求解该问题的全局最优解，给出了具体的算法步骤。
A stochastic global optimization method based on direct search is introduced to solve the global optimal solution of the problem, and the process is also discussed in detail.
就随机并行梯度下降(SPGD)最优化算法在光束净化系统中的应用展开研究。
This paper researches the application of the stochastic parallel gradient descent (SPGD) optimization algorithm on the beam cleanup system.
课程阅读围绕这学期的主题：确定性最优化的随机方法。
Course readings revolve around this term's topic, randomized methods for deterministic optimization.
本文把最优化设计方法和稳健设计思想有机地结合，提出一种基于随机模拟试验的稳健优化设计方法。
Combining optimal design method with robust design principle, a new approach for the robust optimal design is investigated make use of stochastic simulative experiment.
这种学习规则的基本思想就是：通过不断地优化变异随机选择的连接权矩阵元，从而使网络在给定的训练目标下达到整体最优。
The basic idea of this learning rule is to obtain a certain optimization by continuously changing the elements of coupling matrix selected randomly.
正则化图像恢复是条件约束的最优化问题，而小波系数的贝叶斯统计选择是基于图像的随机场观点。
A regularized image restoration is the optimization for some conditional constraint, and the selection of wavelet coefficients based Bayesian statistic is on the image random field view.
本文引进了一种最优化自回归数据滤波算法——卡尔曼滤波来分离这两个分量和消除其他随机噪声。
The paper introduces the Kalman filter method that is an Optimal Recursive Data Processing Algorithm, which can separate these two components and filter out other random noises.
非线性随机系统的最优控制，采用基于性能势的随机优化数值算法。
The optimal control of nonlinear random system adopted random optimal numerical algorithm based on performance potential.
电源优化问题具有高维数、非线性、随机性等特点，常规的优化算法难以求解到最优解。
The generation expansion optimization is high dimension, nun-linear, randomness problems. The convention algorithm hardly finds the best solution.
本文提出了一种在光学自动设计中寻求全局最优化的新方法：随机制抽样法。
Exploring an approach to the search fot global minmum in the multidimensional design space of a lens design with the method of random sampling is presented in the paper.
为了寻求零件最优的结构参数，作者引入损伤度作为判据，建立了零件优化设计与随机疲劳寿命的联系。
Moreover, the author proposed "damage degree" as a criterion in obtaining the optimum structure parameters. A relationship between the optimum design of mechanical elements and r…
该算法在运行过程中增加了随机变异算子，通过对当前最佳粒子进行随机变异来增强粒子群优化算法跳出局部最优解的能力。
The new algorithm includes the mutation operator during the running time which can be useful to improve the ability of PSO in breaking away from the local optimum.
战术层的随机联合优化问题主要是以确定配送时间间隔、配送量和最优配送路线为目标。
The goals of tactical SITIO problem are to determine the distributing period, the allocation of inventory and the optimal vehicle routing.
在求解随机最优路径方面，其问题分两种情形：一种以成功概率为优化目标，搜索约束条件下完成运输任务最大概率的路径；
Two scenarios are considered: one sets the success probability as the objective and tries to find out the route with the maximum probability subject to time constraint;
在求解随机最优路径方面，其问题分两种情形：一种以成功概率为优化目标，搜索约束条件下完成运输任务最大概率的路径；
Two scenarios are considered: one sets the success probability as the objective and tries to find out the route with the maximum probability subject to time constraint;
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