基于混合加权距离的毕达哥拉斯模糊TOPSIS多属性决策方法研究

针对决策信息为毕达哥拉斯模糊集的多属性决策问题，提出了一种基于混合加权测度的TOPSIS决策方法。在分析了现有距离测度方法不足的基础上，首先给出了一种新的毕达哥拉斯模糊距离测度——毕达哥拉斯模糊有序加权距离（PFOWD），并研究了该测度权重的确定方法；在PFOWD基础上，进一步提出了毕达哥拉斯模糊混合加权距离（PFHWD），同时探讨了其特征和与现有毕达哥拉斯模糊测度的关系；最后提出了一种基于PFHWD测度的毕达哥拉斯模糊TOPSIS多属性方法，并用实例验证了方法的有效性和可行性。     

Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

基于遗传算法的装备采购决策优化研究

在装备采购中，由于需求单位地域分布和担负的任务各不相同，对装备的品种、数量、时限要求也就不一样，如何使装备采购科学化、合理化，是一个涉及多变量、多目标的复杂系统问题。在综合考虑装备采购各项因素的基础上，构建多约束条件下的多目标模糊指派模型，提出了基于遗传算法的解决方案，最后通过案例进行仿真实验，验证该算法的可行性和有效性，解决了采用传统优化方法难以解决的装备采购优化决策问题。     

发展是反贫困的核心问题--广西百色反贫困研究之三

发展是反贫困的主题。发展是解决贫困问题的关键。必须用加快发展的办法解决贫困问题。     

农业产业化是我国农业发展的必由之路

农业产业化作为市场经济条件下发展农业的一种主张,引起我国理论界和经济界的广泛关注和讨论.本文从我国的农业现状出发,对我国实施农业产业化的必然性和可行性进行了论证,从而证明农业产业化是我国农业发展的必然选择.同时简要回顾我国农业产业化的发展历程,指出其存在问题,并提出对策,以期农业产业化在我国健康发展.     

在生物进化与人类进化之间

由于人类的进化已日益集中反映在文化的进化上 ,从而使得它与生物之间表现出不同的性质和方式。对于属人世界的生物进化来说 ,既存在自然选择式的客体中介 ,又存在社会选择式的主体中介 ,在当今世界所普遍存在的“生态危机”和各种“人类危机”面前 ,我们更需要确立和谐型的社会选择     

模糊技术在高校教学评估中的研究

研究了高校教学评估中知识的模糊关系及权重问题,讨论在有关模糊隶属函数计算方法的基础上,将模糊技术应用在教学评估中,为教学评估的研究提供一条新的途径。     

模糊集的无穷扩展原理

从普通集族{Xt}t?T（T为无限集）的直积Ⅱt?TXt出发，给出模糊集族At?F(Xt)(t?T)的直积概念；将通常的映射?：Ⅱt?TXt→Y扩展为模糊集族的直积Ⅱt?TF(Xt)到Y上的模糊集F(Y)的映射。     

近代中国整合中西文化冲突的价值选择模式及现代启示

中国近代文化变迁的过程 ,在一定意义上表现为中西文化的冲突与整合的过程。为整合中西文化冲突 ,近代中国先后采取了文化传统主义、文化折中主义、文化虚无主义与文化综合创新等价值选择模式。近代文化整合的经验教训启示我们 ,在新时期文化建设问题上 ,必须正确处理文化的一元与多元、整体性与可分性、时代性与民族性的关系 ,坚持走综合创新之路     

On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models

While much used in practice, latent variable models raise challenging estimation problems due to the intractability of their likelihood. Monte Carlo maximum likelihood (MCML), as proposed by Geyer & Thompson (1992 ), is a simulation-based approach to maximum likelihood approximation applicable to general latent variable models. MCML can be described as an importance sampling method in which the likelihood ratio is approximated by Monte Carlo averages of importance ratios simulated from the complete data model corresponding to an arbitrary value of the unknown parameter. This paper studies the asymptotic (in the number of observations) performance of the MCML method in the case of latent variable models with independent observations. This is in contrast with previous works on the same topic which only considered conditional convergence to the maximum likelihood estimator, for a fixed set of observations. A first important result is that when is fixed, the MCML method can only be consistent if the number of simulations grows exponentially fast with the number of observations. If on the other hand, is obtained from a consistent sequence of estimates of the unknown parameter, then the requirements on the number of simulations are shown to be much weaker.