查询词典 stochastic

How to quantitatively determinate the relation between system behaviors and parameters variations was an important problem of systems biology.In order to study robustness of NF-κB signal transduction networks,the parameters of system model were assigned to subject to stochastic distributions.

然而，包括速率常数和反应物初始浓度在内的许多参数都是通过实验测量或者根据文献推断得到的，即使对于那些完全通过实验来估计参数的模型，也不能确定参数的实际值与对应的生物系统是否完全一致。

But above research were all based on network whose parameters and topology structure were determinate. Network system in practical circumstance often presents randomicity because of influence of some factors. So it often causes some errors when apply traditional network flow theory to practical problems. It is significant for improving network flow theory's practicability to optimize network flow in stochastic circumstance. This question has drawn attention.

但以上研究都是在网络参数和拓扑结构固定的网络上进行的优化，而实际环境中的网络系统受多种因素影响往往会表现出随机性，所以传统网络流理论在具体应用时与实际情况会有一定的偏差，如果能在随机环境下对网络流进行优化，对提高网络流理论的实用性和针对性均有重要意义，这个问题已越来越引起关注。

The derivative 〓 t, x (t of the V function along a solution to a determinate ordinary differential system is changed to be 〓V t, x (t for a stochastic ordinary differential system.

众所周知，用Lyapunov直接法来研究随机常微系统的稳定性与研究确定性常微系统的稳定性有许多类似之处，表现在只需要用〓Vt，x(t代替常微系统中V函数沿解的导数〓t，x

In the first part, we focus on some recent developments and current research trends on deterministic and stochastic global optimization.

第一章中，较全面地概述了目前国内外全局优化的发展动态。

We start by covering deterministic and stochastic dynamic optimization using dynamic programming analysis.

我们会以使用动态规划分析来处理确定及随机的动态最适化作为开始。

Then, the relations among the robust optimization model, stochastic optimization model, and deterministic optimization model are analyzed and two algorithms, enumeration method and genetic algorithm are presented.

分析了鲁棒优化模型与确定性优化模型、随机优化模型的关系，并在此基础上给出了求解鲁棒优化模型的两种方法——枚举法和遗传算法。

Firstly, according to the difficulties in the optimization of chemical engineering and the intrinsic disadvantage of deterministic optimization algorithms, this work analyzed the importance and advantage of stochastic algorithms, and proposed some important aspects in research on them. Secondly, genetic algorithm was applied to two problems of data driven modeling, one of which was combination problem, the other was mixed integer nonlinear programming. Thirdly, systemic investigations were made on the basic structure, dynamic behavior and modifications of particle swarm optimization. Lastly, two kinds of proposed PSO algorithms were applied on calculation of phase equilibrium, which is nonconvex optimization.

本文首先根据化工优化中存在的困难和确定性优化算法内在的缺点，分析了随机优化算法的重要性，并提出研究随机优化算法应注意的问题；其次，将遗传算法应用于两个数据驱动建模问题，一为组合优化问题，一为混合整数优化问题；再次，从粒子群优化算法的基本结构、运动行为、改进方法做了系统的研究：最后，将提出的两种改进粒子群优化算法应用于相平衡计算问题，为非凸全局优化问题。

The codes of the two algorithms are implemented by Visual C++ on Visual Studio 6.0. Optimization software Lingo 9.0 is utilized in the code to solve the deterministic optimization model and two-stage stochastic optimization model.

以Visual Studio6.0为平台，以Visual C++为开发语言编写了两种算法的代码，代码中通过调用Lingo9.0来求解确定性优化模型和两阶段随机优化模型。

To facilitate the practical application, some simple rules are summarized to indicate the optimal selection rule among the ones known so far, under different circumstances. 4 Through clearly describing the uncertainties in stochastic simulation optimization and deterministic complex optimization, a unified formulation is proposed for the two types of optimization problems.

为方便工程应用，总结出一些简单规则，指出各种情形下的目前已知最优挑选规则。4通过明确刻画随机仿真优化与复杂确定性优化问题中的不确定因素，为两类问题提供统一描述。

In history and reality, the contradiction between needs and limited resource always exists. From the view point of system-science, it can be concluded that:①The interaction among need-contradiction-ability is the perpetual force for enterprise evolution (and the evolution of other living systems). The improvement of "ability"is the process and result of evolution.②The competition and coordination among systems is the direct power for enterprise evolution. Competition produces various system actions. Coordination makes systems complement their functions with each other. The interactions of competition and coordination push enterprise into the flow of evolution and optimization.③The"stochastic fluctuation"of the system, which is accidental finding, is another force for enterprise evolution. It is a deterministic element in the process of the self-organization of enterprise.

在历史和现实中，需要和满足需要资源稀缺之间的"问题"是恒久存在的，从系统科学的角度可以演绎出：①需要一问题一能力三种异质要素的相互作用是企业进化（其它一切有生命系统进化）的永恒动力，"能力"的提高正是进化的过程和结果；②各系统间的竞争与协同是企业进化的直接动力，竞争产生多样化的系统行为，协同则使各系统间优势互补，竞争与协同交互作用推动企业呈现出进化与优化的趋势：③系统的"随机涨落力"即意料之外的发现与创新是推动企业进化的又一种动力，它是企业自组织过程中带有必然性的力量：④在解决"问题"中创新者的作用不可低估，但更为根本地说创新者只是"需要一问题一能力"这三种要素相互作用的载体，个体的创新是企业自组织过程的必然结果。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。