Stochastic signal analysis: Numerical representation and applications to system theory

Alternative procedures for representing a stochastic signal as a denumerable series with uncorrelated terms are developed. The study includes considerations about parametric rescaling and its repercussions on the orthogonal expansion, as well as numerical techniques for approaching such an expansion. In addition, applications to the Gaussian characterization of stochastic signals, filtering and stochastic modelling are treated.     

A Genetic Search for Exploiting a Fuzzy Preference Model of Portfolio Problems with Public Projects

When an organization solves a portfolio problem with public projects evaluated by multiple criteria, in which the economic dimension is not essential or not well characterized, the classical methods are not useful. We propose a non-linear preference model developed from normative Value Theory and using fuzzy sets to model some sources of imprecision. This model can be considered as a generalization of the classical approaches. However, the optimization problem is very complex in order to be solved with non-linear programming techniques. Therefore, the model is exploited by an evolutionary algorithm, able to achieve a strong improvement of the quality of solution.     

Global and uniform convergence of subspace correction methods for some convex optimization problems

This paper gives some global and uniform convergence estimates for a class of subspace correction (based on space decomposition) iterative methods applied to some unconstrained convex optimization problems. Some multigrid and domain decomposition methods are also discussed as special examples for solving some nonlinear elliptic boundary value problems.

     

Toward Fuzzy Optimization without Mathematical Ambiguity

Fuzzy programming has been discussed widely in literature and applied in such various disciplines as operations research, economic management, business administration, and engineering. The main purpose of this paper is to present a brief review on fuzzy programming models, and classify them into three broad classes: expected value model, chance-constrained programming and dependent-chance programming. In order to solve general fuzzy programming models, a hybrid intelligent algorithm is also documented. Finally, some related topics are discussed.     

ON HYPERBOLIC TIME DISCOUNTING IN EXHAUSTIBLE RESOURCE MODELS: AN APPLICATION TO WORLD OIL RESOURCES

ABSTRACT. Recent research on discounting in long term economic models involves hyperbolic discounting, in which the marginal discount rate shrinks as time passes. To investigate hyperbolic discounting and exhaustible resource allocation, this work develops a discrete‐time world oil model and model solution procedure, then uses the model to examine the consequences of adopting conventional (constant annual) discounting when hyperbolic discounting is appropriate, of adopting one hyperbolic discount rate path when a different hyperbolic path is appropriate, and of adopting hyperbolic discounting when conventional discounting is appropriate. Five conventional and two hyperbolic discount rate paths are considered. One hyperbolic path is that used by Nordhaus and Boyer [2000]; the other is that recommended by Weitzman [2001]. The generality of the findings is also assessed.     

有界变量线性规划的基线算法

本文对有界变量线性规划的算法进行了研究，得到了一种解此问题的新算法。文中根据基线算法的算法原理，通过对BL表的旋转，在各变量满足界约束的条件下，使目标函数值不断增大，直至得到有界硬上界，从而得到问题的最优解。文中给出了有界变量线性规划基线算法的计算步骤，并给出了一个例子。与单纯形法相比，采用基线算法解有界变量线性规划操作更简单。迭代次数少，解题速度更快。     

叶型优化在非定常流动条件下效果的数值分析

本文对叶型优化在非定常流动条件下的效果进行数值研究。叶型优化基于比较成熟的在定常流动条件下的反问题解法，非定常流动则考虑到动静叶栅之间的相互作用，非定常流动的求解在Ｎ－Ｓ方程的基础上采用分区计算的方法来完成。数值计算的结果表明，定常流动条件下优化得到的叶型在非定常流动条件下同样具有较好的气动性能。     

平行机半在线排序问题研究(Ⅰ)

对半在线平行机排序问题的研究进展作了详细综述和进一步探讨。文章给出半在线排序问题的背景、定义、分类和求解。介绍它们定义和在不同机器环境和目标函数下半在线排序问题分类，以及第一类半在线模型的近似算法的设计及其竞争比分析。     

IFS-BN结合的辐射源威胁评估方法

针对辐射源威胁评估实时性、复杂性和信息不确定性的问题,将直觉模糊集与贝叶斯网络相结合,提出一种基于IFS-BN的辐射源威胁评估方法。IFS-BN算法通过直觉模糊集刻画贝叶斯网络的节点信息,充分描述节点信息的确定性,不确定性以及犹豫度。同时,将直觉模糊集求得的各辐射源先验概率代入贝叶斯网络模型之中,得出后验概率进行威胁等级排序,并在此基础上建立了相应的评估指标体系。仿真分析表明,算法具有较好的可行性与有效性。与经典贝叶斯网络评估方法相比,IFS-BN算法的结果更加客观合理。     

借助Matlab遗传算法反演火山区压力源参数

首先分析了Mogi模型的经典最小二乘反演由于出现矩阵奇异或接近奇异而发散的局限性,决定采用具有全局高效搜索性和优化计算时不需要求导的遗传算法来反演火山区压力源参数。反演程序调用Matlab7.0 Release14工具箱中的选择、交叉和变异等相关程序。最后应用该程序和长白山天池火山区水准和GPS观测资料联合反演火山区压力源参数,经过比较遗传算法和阻尼最小二乘方法的反演结果,可看出2种方法结果近似,可见此方法方便快捷地实现了目标函数的最小化。