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混合智能
相关语句
  mixed intelligent
    This paper mainly discusses the research on the problems of the timetable and auto-generating test paper in university management by using the mixed intelligent algorithm.
    本文主要利用混合智能算法对高校管理中时间表和自动组卷问题进行了研究。
短句来源
    And the paper has conducted more thorough research by using the mixed intelligent algorithm to the university class schedule application, the test paper grouping and the test time arrangement.
    并利用混合智能算法对高校排课时间表问题、考试组卷问题与考试时间安排问题进行了较为深入的研究。
短句来源
    Desgined a mixed intelligent arithmetic by using the technique of combing the stochastic simulation with fuzzy simulation and particle swarm optimization algorithm to solve this kind of problems.
    基于粒子群算法运用随机模拟和模糊模拟相结合的技术,给出了一种求解该规划模型的混合智能算法。
短句来源
    A mixed intelligent arithmetic for random expected value model is designed and simulation is done aiming at revenue-sharing contract coordination model,and the impacts of variety of revenue-sharing contract parameters on supply chain members' performance are discussed.
    设计了求解随机期望值模型的混合智能算法,进行了收入共享契约协调模型仿真实验,探讨了收入共享契约参数变化对供应链成员绩效的影响。
短句来源
    In this paper a stochastic dependence-chance programming model on the portfolio selections based on the modern portfolio theory by Markowitz. Meanwhile, it also discusses how to solve this kind of models by one of the mixed intelligent algorithms which integrate stochastic simulation, genetic algorithms and simulated annealing.
    本文是在Markowitz现代投资组合理论的基础上,建立了证券投资组合的随机相关机会规划模型,并讨论了综合随机模拟、遗传算法和模拟退火算法的混合智能算法对此模型的求解.
  “混合智能”译为未确定词的双语例句
    Hybrid algorithm for complex project scheduling
    一种复杂项目调度问题的混合智能算法
短句来源
    On Integrated Chance Constraints Model of Stochastic Program
    随机规划ICC(β)模型的混合智能算法
短句来源
    Application of Hybrid Strategies to Capacitated Vehicle Routing Problem with Time Windows
    混合智能算法在CVRPTW中的应用
短句来源
    Based on the uncertainty programming theory, we provide the expected value model, Chance-constrained programming model, and Dependent-chance programming model for this system, bring forth the hybrid intelligentalgorithm for the three problems, validate the feasibility by examining the experiment example, and lastly achieve good results.
    根据不确定规划的原理,我们给出了该随机系统的期望值模型,机会约束模型,相关机会模型,并给出了该三类问题的混合智能算法,并通过实例来验证算法的合理性,得到了较好的结果。
短句来源
    In view of the sole intelligent algorithm existing problems and insufficiency, the author designs three kinds of mixed intelligences algorithm: the Chaos Genetic Algorithms, Heredity Annealing Algorithm, Heredity Simulation Annealing Algorithm.
    针对单一智能算法存在的问题和不足,设计混沌遗传算法、遗传退火算法、遗传模拟退火算法三种混合智能算法。
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The expectation disposal model of the uncertain program, probability measurement analysis model, success of maximal event probability model are analysed and determined. Multiplayer program model, uncertain Monte-Carlo program, the interval of decision-making and weight program of decision-making and weight program of the uncertain multiplayer attribute, also the program of uncertain structure neural network optimal model in the uncertain environment are given,. And provide the intelligent algorithm of the uncertain...

The expectation disposal model of the uncertain program, probability measurement analysis model, success of maximal event probability model are analysed and determined. Multiplayer program model, uncertain Monte-Carlo program, the interval of decision-making and weight program of decision-making and weight program of the uncertain multiplayer attribute, also the program of uncertain structure neural network optimal model in the uncertain environment are given,. And provide the intelligent algorithm of the uncertain program is given aswell. Because the appearance and substance of the uncertain complicated problem are different., the way and method of effective analysis and evaluation are also different. So the scientific description and quantitative analysis are not only the further premise of the problem, but also the safeguard of the complicated system抯 optimization.

分析并建立不确定规划的期望值处理模型,机会测度分析模型,极大化事件实现机会的数学模型。给出不确定环境下多层规划的数学模型,不确定Monte-Carlo规划,不确定多属性决策区间熵权规划以及不确定结构规划的神经网络优化模型。并给出不确定规划的混合智能算法。不确定复杂问题决策系统表象和实质各异,有效分析、评价的方式和方法多样,定量的和科学的描述与解析既是问题的深入的前提,也是复杂系统得以优化的保障。

We investigate a more practical assignment problem under fuzzy environment, that is, the elements of profit matrix and time matrix in the assignment problem are fuzzy variables. To obtain a directive decision, we construct a mathematical model for the fuzzy assignment problem based on chance-constrained programming and dependent-chance programming in fuzzy environment. In addition, since there are many complex fuzzy variables in the mathematical model, we design the tabu search algorithm to solve the model based...

We investigate a more practical assignment problem under fuzzy environment, that is, the elements of profit matrix and time matrix in the assignment problem are fuzzy variables. To obtain a directive decision, we construct a mathematical model for the fuzzy assignment problem based on chance-constrained programming and dependent-chance programming in fuzzy environment. In addition, since there are many complex fuzzy variables in the mathematical model, we design the tabu search algorithm to solve the model based on fuzzy simulation. Finally, we give a numerical example to show the efficiency of the algorithm.

研究了一类更加贴近于现实生活的模糊环境中的指派问题,即利润矩阵和时间矩阵中的元素均为模糊变量的指派问题。并借鉴针对模糊环境中的优化问题提出的机会约束规划模型和相关机会规划模型的思想,建立了模糊指派问题的数学模型。此外,考虑到模型涉及大量具有复杂性和多样性的模糊变量,设计了一种混合智能算法,即基于模糊模拟的禁忌搜索算法来求解模型的近似最优解。最后,通过一个算例说明了所建立的模型和所设计算法都是行之有效的。

This paper considers how to increase the capacities of the elements in a set E efficiently so that probability of the total cost for the increment of capacity can be under an upper limit to maximum extent, while the final expansion capacity of a given family F of subsets of E has a given limit bound.The paper supposes the cost is a stochastic variable with some distribution.Network bottleneck capacity expansion problem with stochastic cost is originally formulated as dependent-chance programming model according...

This paper considers how to increase the capacities of the elements in a set E efficiently so that probability of the total cost for the increment of capacity can be under an upper limit to maximum extent, while the final expansion capacity of a given family F of subsets of E has a given limit bound.The paper supposes the cost is a stochastic variable with some distribution.Network bottleneck capacity expansion problem with stochastic cost is originally formulated as dependent-chance programming model according to some criteria.For solving the stochastic model efficiently,network bottleneck capacity algorithm,stochastic simulation and genetic algorithm are integrated to produce a hybrid intelligent algorithm.Finally a numerical example is presented.

文章研究的问题为,在不确定环境中,怎样去增加网络中一组边的容量到一个指定的容量,以至于网络瓶颈扩张的费用不超过给定的总费用上限的概率尽可能的大。本文假定每一条边的单位扩张费用Wi是一个随机的变量,它服从一定的概率分布。带有随机单位扩张费用W的网络瓶颈容量扩张问题可以根据一些规则,列出它的相关机会规划模型的通用表达式。随后,本文将网络瓶颈容量算法、随机模拟方法和遗传算法合成在一起,设计出该问题的混合智能通用算法。最后,给出数值算例。

 
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