地铁车站火灾风险的模糊综合评价

根据地铁车站火灾的特点,确定地铁车站火灾风险的评价指标体系及权重.运用模糊数学原理,建立地铁车站火灾风险的模糊综合评价模型,并应用模型对某地铁车站火灾风险进行评价,较好的反映该地铁车站火灾安全的实际状况.方法为地铁车站火灾风险评价提供了新的思路,也为地铁车站火灾的防治提供理论依据.     

基于改进风险度模型的高层建筑施工安全风险评价研究

为了评价高层建筑施工过程中的安全情况,提出了一种基于改进风险度模型的风险评价方法.首先,选择16种常见的安全事故类型构建评价的指标体系.其次,针对传统风险度模型的不足,在"概率"和"影响"的基础上,增加"不可预测性"这一风险属性,并结合三角模糊数,提出了一种基于改进风险度模型的高层建筑施工安全风险评价方法.最后,通过实例验证了方法的可行性和有效性.     

公众聚集场所火灾风险的模糊综合评价方法研究

公众聚集场所的火灾风险有其自身的特点.在分析公众聚集场所火灾风险因素的基础上,探讨了该场所火灾风险的模糊综合评价方法,为该场所火灾风险评价提供一种方法尝试,为火灾风险管理提供理论上的、量化的依据.     

基于改进风险矩阵法的医院建筑火灾风险评价

在对国内外广泛采用的风险矩阵法进行全面系统的分析和总结的基础上,针对风险矩阵方法的不足,结合医院建筑火灾风险评价的的客观实际和需要,对风险矩阵法进行了改进,并将风险矩阵法引入医院建筑的火灾风险评价,提出一种融合改进风险矩阵法与层次分析法的医院建筑火灾风险评价模型,并实例验证该模型的有效性和可行性.     

基于Delphi-可拓理论的大型酒店消防安全评价研究

针对大型酒店消防管理的复杂性,综合考虑高层建筑空间火灾发生及消防救援的特点,建立全面多系统的消防安全综合评价指标体系.采用Delphi法将定性指标量化,引入物元可拓理论分析法构建综合可拓评价模型,得到各准则层及系统整体的风险等级和偏离所属等级的趋向程度.以长春市某大型酒店为例,验证了该体系的科学性和可行性.     

模糊最优归类模型在服务过程质量评价中的应用

介绍模糊最优归类模型,针对服务质量差距环式模型,应用模糊最优归类模型计算服务过程各环节质量指标值及各环节之间差距值,并且引入模糊最优归类模型提高差距分辨的方法,以提高质量指标值计算的水平.     

基于扰动模糊综合评价法的企业BI项目实施风险评估研究

商业智能(BI)系统能够帮助企业经营者进行智能化的决策,但在一个日益复杂的企业运营环境中,BI系统的实施如同ERP系统一样,面临着巨大的问题、压力和高风险.针对以上特性,提出了一种BI项目实施风险扰动模糊综合评价模型和方法,以上海新媒体A公司的BI项目为实例对企业BI项目实施中的各相关风险因素进行了分析,最优判别,得出了导致风险的主要因素,同时为BI实施风险规避策略提供科学依据.     

基于犹豫模糊语言环境的投资组合优化研究

跟传统模糊投资组合相比,基于犹豫模糊语言环境的投资组合不仅可以使用自然语言对金融资产及其不确定程度进行评价,还可以避免评价过程中信息的丢失。本文根据犹豫模糊语言投资组合综合评价系统,对不同金融产品计算得分。通过设置不同的语言尺度函数的参数值及犹豫模糊语言优化模型的临界值,针对激进型、稳健型和保守型三类投资者分别提出了收益最大化和风险最小化犹豫模糊语言投资组合模型,对建立的非线性模型进行求解,得到犹豫模糊语言投资组合的最优解。最后,用数值仿真验证了模型的合理性和有效性。     

基于模糊RBF神经网络的单位廉政风险评价研究

廉政风险防控是反腐败工作的新举措,对风险的评价是风险管理中一个不可缺少的流程.首先运用文献研究方法,提出廉政风险评价的初步指标,再运用帕累托理论得到经过约减的指标体系.建立基于模糊RBF神经网络的廉政风险水平评价模型.并在某军区对20个单位的廉政风险水平进行评价,与传统的评价模型相比,模型能够克服评估过程中的随机性和不确定性,以及计算过程中容易陷入局部极小的问题.     

基于直觉模糊数的承包商资格预审模型

业主在招标过程中需要对承包商进行资格预审评价,其本质是一个多指标群决策问题。然而由于客观情况的复杂多变性和主观思维的不确定性,专家在决策过程中存在犹豫度,基于此,本文采用直觉模糊数理论建立承包商资格预审模型,运用模糊熵方法计算评价指标的最优权重,在此基础上,采用直觉模糊加权平均算子集结决策者的评价意见,进而对各个承包商综合评价结果进行排序,选出最优承包商,解决传统模糊集不能完整表达所研究问题的全部信息问题,最后通过算例验证该模型的有效性。     

A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms

Location of fire stations is an important factor in its fire protection capability. This paper aims to determine the optimal location of fire station facilities. The proposed method is the combination of a fuzzy multi-objective programming and a genetic algorithm. The original fuzzy multiple objectives are appropriately converted to a single unified ‘min–max’ goal, which makes it easy to apply a genetic algorithm for the problem solving. Compared with the existing methods of fire station location our approach has three distinguish features: (1) considering fuzzy nature of a decision maker (DM) in the location optimization model; (2) fully considering the demands for the facilities from the areas with various fire risk categories; (3) being more understandable and practical to DM. The case study was based on the data collected from the Derbyshire fire and rescue service and used to illustrate the application of the method for the optimization of fire station locations.     

Rule generation for hierarchical collaborative fuzzy system

A new method of rule generation for the hierarchical collaborative fuzzy system, HCFS, is proposed. This HCFS is structured like various parallel fuzzy subsystems and it overcomes the dimensionality problem and the lack of interpretability of most of the traditional fuzzy systems, when dealing with complex real-world problems. An association process of different fuzzy systems is presented in this work, through the use of a relevance concept of a fuzzy system. The result of this aggregation is a collaborative structure where all sub-models have the ability to gradually improve the overall accuracy of approximation by adding their own contributions. For this structure we propose a new algorithm to be used in the procedures of the three learning phases: the structure building, the parametric identification and the division of the learning data among the various levels of the hierarchical structure. This new fuzzy modelling technique automatically generates and tunes the sets of fuzzy rules in the hierarchical collaborative structure (HCS). The effectiveness of the proposed HCFS model in handling high-dimensional and complex problems is demonstrated through various numerical simulations.     

Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering,GA and unscented filter

This paper presents a two stage procedure for building optimal fuzzy model from data for nonlinear dynamical systems. Both stages are embedded into Genetic Algorithm (GA) and in the first stage emphasis is placed on structural optimization by assigning a suitable fitness to each individual member of population in a canonical GA. These individuals represent coded information about the structure of the model (number of antecedents and rules). This information is consequently utilized by subtractive clustering to partition the input space and construct a compact fuzzy rule base. In the second stage, Unscented Filter (UF) is employed for optimization of model parameters, that is, parameters of the input–output Membership Functions (MFs).     

火场-结构联合分析简化模型及其应用研究

为在建筑结构火灾反应分析中考虑实际火场特性,并简化火场分析模型与结构有限元分析模型之间的复杂对应关系,提出并建立了火场温度及对流辐射边界的热传导分析时空模型(STM)和基于时空模型的火场-结构联合分析方法。该方法根据火场模型计算出室内火场温度分布场以及对流辐射边界场离散数据,通过双向正交多项式进行拟合来得到不同时刻的构件边界连续时空模型,再通过时空模型进行热传导分析和热力耦合分析,从而实现火场-结构联合分析方法。在验证其合理性的基础上,通过ABAQUS子程序UTEMP和DFLUX实现其分析过程,并进行了北京某档案馆工程的应用分析,结果表明该方法可以较好地联合火场模拟与结构分析用于结构火灾安全评价。     

Supervised fuzzy pattern recognition

This paper is devoted to the problem of supervised fuzzy pattern recognition. The cases with non-fuzzy and fuzzy labels are considered. Based on the properties of linearly separable fuzzy classes, some algorithms are proposed for building matching functions of these classes. All algorithms are computer oriented and can be implemented for the automatic recognition of fuzzy patterns.     

基于输入-输出数据的重心法模糊系统及其泛逼近性

研究基于输入-输出数据的重心法模糊系统及其泛逼近性.首先,将一组单输入-单榆出数据转换成模糊推理规则,通过选择适当的模糊蕴涵算子生成模糊关系,再利用这种模糊关系求出二雄随机变量的联合概率密度函数.当将模糊蕴涵分别取为Larsen蕴涵和Mamdani蕴涵时,分别得到了两种具体的概率密度函数(称之为Larsen分布和Mamdani分布).其次,利用这两种概率分布.分别求出了对应的两种回归函数,指出这种回归函数实际上是模糊控制中的重心法模糊系统.我们分别给出了这种模糊系统具有泛逼近性的充分条件.从而进一步揭示了模糊系统的概率论意义.     

基于输入-输出数据的概率分布和数字特征

利用模糊推理建立了一种基于输入-输出数据构造联合概率密度函数的方法.首先,将一组单输入-单输出数据转换成模糊推理规则,通过选择适当的模糊蕴涵算子生成模糊关系,再利用这种模糊关系求出二维随机变量的联合概率密度函数.当将模糊蕴涵分别取为Larsen蕴涵和Mamdani蕴涵时,分别得到了两种具体的概率密度函数(称之为Lars...     

Fuzzy matrix games via a fuzzy relation approach

A generalized model for a two person zero sum matrix game with fuzzy goals and fuzzy payoffs via fuzzy relation approach is introduced, and it is shown to be equivalent to two semi-infinite optimization problems. Further, in certain special cases, it is observed that the two semi-infinite optimization problems reduce to (finite) linear programming problems which are dual to each other either in the fuzzy sense or in the crisp sense.     

Self-tuning of fuzzy belief rule bases for engineering system safety analysis

A framework for modelling the safety of an engineering system using a fuzzy rule-based evidential reasoning (FURBER) approach has been recently proposed, where a fuzzy rule-base designed on the basis of a belief structure (called a belief rule base) forms a basis in the inference mechanism of FURBER. However, it is difficult to accurately determine the parameters of a fuzzy belief rule base (FBRB) entirely subjectively, in particular for complex systems. As such, there is a need to develop a supporting mechanism that can be used to train in a locally optimal way a FBRB initially built using expert knowledge. In this paper, the methods for self-tuning a FBRB for engineering system safety analysis are investigated on the basis of a previous study. The method consists of a number of single and multiple objective nonlinear optimization models. The above framework is applied to model the system safety of a marine engineering system and the case study is used to demonstrate how the methods can be implemented.     

The influence of some fuzzy implication operators on the accuracy of a fuzzy model-part II

The influence of fuzzy implication operators and the connective Also on the accuracy of a fuzzy model of a d.c. series motor is considered. Some typical fuzzy implication operators are applied to the construction of a fuzzy model of a d.c. series motor. A root-mean-square error is used as the criterion of the fuzzy model's adequacy to the real system. A number of mathematical operations necessary for the implementation of the fuzzy model are used as the criterion by which the fuzzy model's applicability if estimated from the point of view of computing techniques. The best types of fuzzy relations, representing fuzzy models of a real system, are chosen in order to secure the least root-mean-square error with minimal number of mathematical operations necessary for computer implementation.