多目标线性规划饲料配方模型设计

针对当前使用线性规划模型中存在的问题，分析了饲料配方计算问题的特征，并提出饲料配方的多目标线性规划模型。然后，以一个对比试实例说明求解这个模型能提供一组可行解，且表明在计算机辅助饲料配方计算机领域中，求解多目标规划模型比求解线性规划模型得到的配方优越。     

工程方案的评价与排序的多准则分析

本文在充分考虑水利水电工程开发方案的评价与决策过程中的任务和特点的基础上,根据由多个专家参与方案的多准则评估所得的分布式评价矩阵,借助于多准则决策的PROMETHEE方法建立各方案向的级别不劣于关系,并研究了相应的决策方法进行方案的排序,最后给出了实例研究成果。     

投资决策分析中的几个问题研究

本文就投资决策分析中的几个薄弱环节——投资项目的优良程度分析资金的时间价值分析、投资风险分析等提出了符合实际需要的看法.     

带钢表面检验中的缺陷分类

文章论述了一种使用质量检测软件决策树对带钢表面缺陷进行分类的方法。     

基于时间矩阵序列的多指标方案综合排序分配法

针对多指标方案排序选优决策问题,提出了一种基于时间矩阵序列的方案综合排序分配法.假设存在一个"隐评价函数",它能对方案的各种优先排序进行综合评价,由此得到方案的最优排序和最优决策方案.利用方案的时序信息,构造了"隐评价函数"关于方案排序的"综合效率"评价矩阵,提出了方案综合排序选优分配模型,5个供应商6个指标的综合排序案例表明该方法合理、可行.     

群体大小与群体创建决策方案数量的实验研究

针对口头面对面群体大小群体创建决策方案数量的影响进行了实验研究,实验主体包括3组平均年龄为21岁的本科生和1组平均年龄为33岁的工商管理硕士生,实验将各组分成1－17人不同大小的群体,任务属于想法产生类任务,实验结果表明,在该方式下群体创建方案的数量随群体人数增大呈中间高两头低的“n”形曲线变化,人均方案数量随群体人数增大而减小。     

"数字清江"工程实现策略及进展(Ⅰ)

从数字流域的基本思想出发,提出了“数字清江”工程建设的主要内容、体系结构及其实现策略,并对其实现技术进行了探讨,在此基础上,介绍了“数字清江”工程建设的主要进展。     

A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes

A critical issue for the application of Markov decision processes (MDPs) to realistic problems is how the complexity of planning scales with the size of the MDP. In stochastic environments with very large or infinite state spaces, traditional planning and reinforcement learning algorithms may be inapplicable, since their running time typically grows linearly with the state space size in the worst case. In this paper we present a new algorithm that, given only a generative model (a natural and common type of simulator) for an arbitrary MDP, performs on-line, near-optimal planning with a per-state running time that has no dependence on the number of states. The running time is exponential in the horizon time (which depends only on the discount factor and the desired degree of approximation to the optimal policy). Our algorithm thus provides a different complexity trade-off than classical algorithms such as value iteration—rather than scaling linearly in both horizon time and state space size, our running time trades an exponential dependence on the former in exchange for no dependence on the latter.Our algorithm is based on the idea of sparse sampling. We prove that a randomly sampled look-ahead tree that covers only a vanishing fraction of the full look-ahead tree nevertheless suffices to compute near-optimal actions from any state of an MDP. Practical implementations of the algorithm are discussed, and we draw ties to our related recent results on finding a near-best strategy from a given class of strategies in very large partially observable MDPs (Kearns, Mansour, & Ng. Neural information processing systems 13, to appear).     

Vector Image Segmentation by Piecewise Continuous Approximation

In this article we present an approach to the segmentation problem by a piecewise approximation of the given image with continuous functions. Unlike the common approach of Mumford and Shah in our formulation of the problem the number of segments is a parameter, which can be estimated. The problem can be stated as: Compute the optimal segmentation with a fixed number of segments, then reduce the number of segments until the segmentation result fulfills a given suitability. This merging algorithm results in a multi-objective optimization, which is not only resolved by a linear combination of the contradicting error functions. To constrain the problem we use a finite dimensional vector space of functions in our approximation and we restrict the shape of the segments. Our approach results in a multi-objective optimization: On the one hand the number of segments is to be minimized, on the other hand the approximation error should also be kept minimal. The approach is sound theoretically and practically: We show that for L ²-images a Pareto-optimal solution exists and can be computed for the discretization of the image efficiently.     

基于NSGA-II的改进多目标遗传算法

在已有多目标优化算法(NSGA-II)研究和分析的基础上,为加快收敛速度,提高收敛精度,设计了新的初始筛选机制,改进了交叉算子的系数生成,提出了更为合理的排挤机制。通过典型应用函数的计算测试,结果表明:上述改进不仅具有较高的计算效率,而且能够得到分布更为合理的解,且能保持解的多样性分布。