粒子群算法惯性算子凹凸性分析

粒子群算法的惯性因子是算法中的一个重要的参数,目前的研究结果表明,惯性因子为减函数时算法的运行效果更为良好。文中提供了四种减函数作为惯性因子可以使用的算子,它们的凹凸性各有不同。对四个算例的数值仿真结果表明,表现最好的是惯性因子先上凸后下凸的PSO,惯性因子为下凸函数的PSO综合表现优于惯性因子为上凸函数的情况。     

基于模糊综合评判的在役施工升降机安全事故分析

施工升降机是建筑工地常用的建筑起重机械。由于施工现场环境恶劣,人货混用普遍,导致升降机安全问题频发。本文分析了近年来升降机安全事故的主要类型及发生原因,并结合模糊综合评判法,对在役升降机的安全事故的原因进行了模糊评判。     

基于模糊的高层建筑施工安全评价研究

在建筑业高速发展的同时,高层建筑也迅速崛起。然而对于高危行业的建筑业,整个生命周期过程中都存在着很多的风险。因为在高层的施工中存在作业面较高,所用的机械设备和安全设施更多,作业周期更长等未知情况。文章中经研究分析确定了影响高层建筑施工安全的重要因素,通过对因素的分析划分构造指标体系,再利用层次分析法对各指标进行权重的确定,最后利用模糊数学原理对它进行评价。结合施工企业实例,利用综合评价模型得出了影响该企业安全施工的重要因素,提供了改进的方向和建议。     

Moving beyond the tensions in open environmental innovation towards a holistic perspective

This paper analyses factors determining open environmental innovation (EI) and explores the possibility of embracing their tensions to achieve integrated environmental strategic goals. Building on recent literature linking open innovation and EI, the study assumes open EI strategic goals as a multidimensional construct based on the “synergistic and interwoven nature” of four identified enabling factors, that is, partners, knowledge, benefits, and motivations. A fuzzy‐set qualitative comparative analysis (fsQCA) based on Boolean algebra is conducted to analyse a set of 186 European companies operating in the telecommunication and information and communication technology (ICT) service industries. Making important theoretical, managerial, and policy contributions, this paper offers insights into the key factors enabling open EI and the tensional relationships within and between them. In doing so, it captures the non‐linear effects among these factors and the strategic goals related to an open EI strategy. It also suggests how the paradox theory might help in addressing the tensional dilemma of open EI strategy.     

基于模糊评价模型的物流管理专业实践教学体系研究

考虑物流管理专业实践教学的特性,从实践环节、创新与创业、管理机制、教学团队等方面构建了实践教学指标体系,并运用模糊综合评价法进行评价分析。研究结论表明,毕业实习、毕业论文、学科竞赛、创新与创业项目对物流管理专业实践教学的影响较大。     

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。     

House price determinants in Athens: a spatial econometric approach

In this paper, we try to identify the price determinants in the biggest real estate market of Greece, the metropolitan area of Athens. For that purpose, various spatial econometric models are used to explore their prediction ability and we are displaying the variations in property prices for the wider area of Athens. These models have been compared based on different criteria such as model fit, the Akaike information criterion and variance of the residuals. Our results indicate that, in our case, the spatial general model is the most appropriate simultaneous autoregressive model when dealing with spatially autocorrelated prices of housing properties data, in terms of our selection criteria.     

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.