Assessing Systemic Importance With a Fuzzy Logic Inference System

Three metrics are designed to assess Colombian financial institutions' size, connectedness and non-substitutability as the main drivers of systemic importance: (i) centrality as net borrower in the money market network; (ii) centrality as payments originator in the large-value payment system network; and (iii) asset value of core financial services. An aggregated systemic importance index is calculated based on expert knowledge by using a fuzzy logic inference system. We use principal component analysis to calculate a benchmark index for comparison purposes. Overall similarities between both indexes put forward that expert knowledge aggregation is consistent with that based on a purely quantitative standard approach. Specific non-negligible differences concur with the nonlinear features of an approach whose intention is to replicate human reasoning. Both indexes are complementary and provide a comprehensive relative assessment of each financial institution's systemic importance in the Colombian case, in which the choice of metrics pursues the macroprudential perspective of financial stability. Copyright © 2015 John Wiley & Sons, Ltd.     

工业锅炉煤改气客户细分模型及其应用——基于层次分析和熵权修正的模糊综合评价理论

煤改气是加快调整工业锅炉能源结构、改善大气环境质量的重要措施之一。燃气运营商欲开拓工业锅炉煤改气市场，应正确辨别终端市场客户的类别。从行业特性、企业特性、锅炉状况和客户开发潜力4个方面构建工业锅炉煤改气客户细分评价指标体系，用AHP－修正熵组合赋权方法确定评价指标权重，建立工业锅炉煤改气客户细分模型并开展应用。评价结果兼顾客观属性与主观偏好，符合客观实际，可为燃气运营商开展差异化管理，采取多元化营销模式提供决策依据。     

Early-warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering

Currency crises, also often called balance-of-payment crises, occur when massive capital outflows force a country to devalue or float its currency. The world-wide integration of capital markets since the 1980s and 1990s has increased the degree of capital mobility, which also determined a substantial turbulence in foreign exchange markets and frequent currency crises. In this paper, we explore advanced supporting instruments for predicting currency crises, based on an empirical study of the currency crisis episodes in 23 emerging markets around the world during the second half of last century. More specifically, we investigate the usefulness of prediction models built based on the fuzzy c-means method. First we build clustering models that partition data into a certain number of overlapping natural groups. Thereafter, we classify the data clusters into early-warning clusters and tranquil clusters. We compare the performance of our models with a conventional c-means clustering model and a benchmark probit model. The results show that the proposed models achieve a similar level of out-of-sample performance as the probit model and c-means model. The fuzzy approach also introduces additional explanatory advantages into the early-warning analysis process. Copyright © 2010 John Wiley & Sons, Ltd.     

A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting

Air transport demand forecasting is receiving increasing attention, especially because of intrinsic difficulties and practical applications. Total passengers are used as a proxy for air transport demand. However, the air passenger time series usually has a complex behavior due to their irregularity, high volatility and seasonality. This paper proposes a new hybrid approach, combining singular spectrum analysis (SSA), adaptive-network-based fuzzy inference system (ANFIS) and improved particle swarm optimization (IPSO), for short-term air passenger traffic prediction. The SSA is used for identifying and extracting the trend and seasonality of air transport demand and the artificial intelligence technologies, including ANFIS and IPSO, are utilized to deal with the irregularity and volatility of the demand. The HK air passenger data are collected to establish and validate the forecasting model. Empirical results clearly points to the enormous potential that the proposed approach possesses in air transport demand forecasting and can be considered as a viable alternative.     

企业应用推广创新方法绩效评价指标体系研究

创新方法是支撑企业创新活动的重要工具,我国自2007年以来,先后在多家企业开展以TRIZ理论、六西格玛管理、QFD、精益生产等单一或多种创新方法的应用推广,以目标为导向,分析企业应用创新方法产生创新绩效影响因素的基础上,确定以"科学性、系统性、可操作性、发展性"为主体的绩效评价指标体系构建原则,建立了科学规范的评价指标体系,应用层次分析法(AHP)计算了指标权重,确定了模糊综合评价方法及过程,提出了创新方法示范企业管理策略,为创新方法管理工作和示范企业培育提供参考和借鉴。     

基于模糊综合评价模型的天津滨海新区城市生态系统健康评估

在分析城市生态系统健康评估研究进展的基础上,采用层次分析法建立城市生态系统健康评价指标体系,然后利用模糊数学模型,以天津市滨海新区为案例,进行城市生态系统健康评估。结果表明,近年来天津滨海新区城市生态系统处于亚健康状态,总体状况并没有明显改善。主要原因是在经济快速增长的同时,第二产业比重过高,能源消耗大量增加,环境质量较差,虽然投资力度加大,但仍有恶化趋势,未来改善压力大。如果不从整体上加大产业结构改善,加大环境保护和生态建设,将大大影响滨海新区的可持续发展。     

有优先权的混贮备可修系统的模糊可靠性分析

研究了有优先权的3个不周型部件和1个修理设备组成的混贮备可修系统,利用普通可靠性和模糊可靠性的基础理论,在转换开关完全可靠的情形下建立了系统模糊可靠性模型,利用模糊数学的原理和方法、马尔可夫型可修系统的研究方法和Maplel 1．0软件,得到了该系统的一些模糊可靠性指标,并给出算例。     

基于AHP和模糊灰色综合评价法的新农村建设融资模式研究

新农村建设包括农村基础设施建设、农业技术改造和农村经济发展等方面,需要庞大的资金支持。本文研究了财政支农、农村金融机构支农和农村非正规金融机构支农政策的实践效果,指出了三种方式的利弊;采用层次分析方法（AHP）对每种新农村建设的融资模式的指标权重进行了分析;然后建立了基于模糊综合评价的新农村建设融资模式的评价方法,并结合具体案例进行了实证分析。通过建立的基于层次分析法和模糊综合评价的新农村建设融资模式评价方法体系,可以帮助决策者更加准确和客观地选择新农村建设的融资模式。     

基于模糊物元的自然灾害应急物流线路优选模型研究

突发性自然灾害中的应急物流线路选择是一个多目标评价问题,因为较强的不确定性和模糊性而较难准确实现。构建了突发性自然灾害中应急物流线路选择评价的指标体系,并主要基于模糊物元分析方法,提出了一种应急物流线路优选的模糊物元模型,为线路优选决策提供定量依据,并给出了计算实例,以验证该模型的有效性和可行性。     

Web服务Qos评价的模糊新算法

质量评价在Web服务生命周期的服务选择、实时监控、管理和计费等阶段占有越来越重要的地位,且模糊理论已应用于该领域。文章从区分冗余隶属度的角度对模糊综合评判算法进行改进,以消除指标隶属度中对目标分类不起作用的冗余数值,在没有冗余数据的条件下实现隶属度转换,并应用于Web服务QoS的评价。