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1.
含有模糊和随机参数的混合机会约束规划模型   总被引:9,自引:0,他引:9  
丁晓东  吴让泉  邵世煌 《控制与决策》2002,17(5):587-590,594
提出一类混合机会约束规划模型,该模型同时含有模糊和随机参数,运用随机模拟与模糊模拟相结合的技术,给出了求解该机会约束规划模型的遗传算法,通过对生产过程最优化决策的典型问题进行分析建模和数值求解,说明了该模型和算法的合理性和有效性。  相似文献
2.
一类含有随机和模糊参数的规划模型   总被引:5,自引:0,他引:5  
提出一类模糊机会约束的随机期望值规划模型,该模型同时含有随机和模糊参数.对改进的“报童问题”进行的分析,说明了模型的合理性.运用随机模拟与模糊模拟相结合的技术,给出了求解该规划模型的遗传算法.并对改进的“报童问题”进行了数值求解,同时给出了其它数值例子,进一步说明了所给出模型的适用性和有效性.  相似文献
3.
Dependent-chance programming: A class of stochastic optimization   总被引:4,自引:0,他引:4  
This paper provides a theoretical framework of dependent-chance programming, as well as dependent-chance multiobjective programming and dependent-chance goal programming which are new types of stochastic optimization. A stochastic simulation based genetic algorithm is also designed for solving dependent-chance programming models.  相似文献
4.
基于交叉熵法解决随机用户和需求车辆路径问题   总被引:4,自引:0,他引:4  
提出一种解决随机用户和需求车辆路径问题(VRPSCD)的方法.针对目标函数的复杂性.设计一种基干Monte-Carlo抽样求解路径期望费用的有效方法;为提高标准交叉熵(CE)法性能,针对用于更新Markov转移矩阵关键路径,根据分位值改变大小,设计了自适应调整方法.计算结果验证了采用该方法解决此问题的鲁棒性和有效性.  相似文献
5.
基于随机模拟与PSO算法相结合的随机机会约束规划算法   总被引:3,自引:0,他引:3  
随机机会约束规划作为一类重要的随机规划,广泛存在于许多领域中.为了寻找更有效的求解随机机会约束规划的算法,通过采用随机模拟来逼近随机函数,并在微粒群算法PSO(Particle Swarm Optimization)中利用随机模拟实现估计适应值和检验解的可行性,从而给出了求解随机机会约束规划的新算法,最后,测试其性能并与遗传算法进行了比较,实例结果表明该算法的正确性和有效性.  相似文献
6.
In this paper we formulate a network design model in which the traffic flows satisfy dynamic user equilibrium conditions for a single destination. The model presented here incorporates the Cell Transmission Model (CTM); a traffic flow model capable of capturing shockwaves and link spillovers. Comparisons are made between the properties of the Dynamic User equilibrium Network Design Problem (DUE NDP) and an existing Dynamic System Optimal (DSO) NDP formulation. Both network design models have different objective functions with similar constraint sets which are linear and convex. Numerical demonstrations are made on multiple networks to demonstrate the efficacy of the model and demonstrate important differences between the DUE and DSO NDP approaches. In addition, the flexibility of the approach is demonstrated by extending the formulation to account for demand uncertainty. This is formulated as a stochastic programming problem and initial test results are demonstrated on test networks. It is observed that not accounting for demand uncertainty explicitly, provides sub-optimal solution to the DUE NDP problem.  相似文献
7.
基于PSO求解随机期望值模型的混合智能算法   总被引:2,自引:1,他引:1       下载免费PDF全文
随机期望值模型是一类有着广泛应用背景的随机规划问题,为了寻找更为高效的求解随机期望值模型的算法,采用随机仿真产生样本训练BP网络以逼近随机函数,然后应用微粒群算法并以逼近随机函数的神经元网络作为适应值估计和实现为了检验解的可行性,从而提出了一种求解随机期望值模型的混合智能算法。最后通过两个实例的仿真结果说明了算法的正确性和有效性。  相似文献
8.
基于二阶段随机规划的不确定条件下过程优化研究   总被引:1,自引:1,他引:0  
在基于二阶段随机规划的不确定条件下过程优化研究中,Ierapetritou and Pistikopoulos(1994)提出了可行域求解策略,Liu and Sahinidis(1996)在此基础上用蒙特卡洛积分策略代替了高斯积分策略,但对于可行域的限定条件尚有欠缺。本文分析和比较了前人的工作,将蒙特卡罗积分策略与基于对偶理论的可行域限定条件相结合,提出了新的求解策略,不仅避免了可行域求解策略中求解一系列子问题而引起的计算负荷随不确定参数数目呈指数增加的不足,而且使蒙特卡洛积分策略算法中的可行域限定条件更加合理,应用文献中的算例进行了仿真实验,证明了该算法的有效性。  相似文献
9.
A scenario-based, multistage stochastic programming model is developed for the management of the Highland Lakes by the Lower Colorado River Authority (LCRA) in Central Texas. The model explicitly considers two objectives: (1) maximize the expected revenue from the sale of interruptible water while reliably maintaining firm water supply, and (2) maximize recreational benefits. Input data can be represented by a scenario tree, built empirically from a segment of the historical flow record. Thirty-scenario instances of the model are solved using both a primal simplex method and Benders decomposition, and results show that the first-stage ('here and now') decision of how much interruptible water to contract for the coming year is highly dependent on the initial (current) reservoir storage levels. Sensitivity analysis indicates that model results can be improved by using a scenario generation technique which better preserves the serial correlation of flows. Ultimately, it is hoped that use of the model will improve the LCRA's operational practices by helping to identify flexible policies that appropriately hedge against unfavorable inflow scenarios.  相似文献
10.
Uncertainty is an inherent characteristic in most industrial processes, and a variety of approaches including sensitivity analysis, robust optimization and stochastic programming have been proposed to deal with such uncertainty. Uncertainty in a steady state nonlinear real-time optimization (RTO) system and particularly making robust decisions under uncertainty in real-time has received little attention. This paper discusses various sources of uncertainty within such closed loop RTO systems and a method, based on stochastic programming, that explicitly incorporates uncertainty into the RTO problem is presented. The proposed method is limited to situations where uncertain parameters enter the constraints nonlinearly and uncertain economics enter the objective function linearly. Our approach is shown to significantly improve the probability of a feasible solution in comparison to more conventional RTO techniques. A gasoline blending example is used to demonstrate the proposed robust RTO approach.  相似文献
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