基于战略对应的IT项目组合选择模型

把信息技术项目当作组合来管理可以通过平衡风险和收益来促进企业目标和IT应用的结合,但由于决策信息的不确定性和IT项目目标与企业战略的难以对应,企业面临IT项目组合选择的挑战。构建基于战略对应的IT项目组合选择模型,其中模糊集和模糊层次分析法用来刻画不确定信息和评估IT项目风险、成本及收益,关键成功因素法用来提高IT项目与企业战略的对应,并建立模糊0－1整数规划。利用定性可能性理论把模糊组合选择模型转化为一般可求解的整数规划形式,最后用一个案例说明模型的用法。     

基于平衡计分卡的IT治理模型研究

企业在实施IT治理过程中,对于项目的评价、开发、实施以及维护,需要一种多指标的方法来进行目标分解。平衡记分卡可以帮助企业来分解目标,同时根据对于目标的绩效考察来驱动战略。首先介绍IT治理的概念、模型以及IT平衡记分卡的四个维度,接着提出了一个Cobit与IT平衡计分卡相结合的瀑布模型,解决了IT治理中的绩效评价问题,使得IT治理过程更加完善和成熟。     

基于优势-直觉模糊粗糙模型的IT项目绩效审计评价规则获取

针对我国政府审计机关对政府投资的IT项目进行绩效审计评价规则知识获取的困难,考虑了条件属性取值为优势精确值、分类结果为直觉模糊值的决策系统规则获取问题.首先比较条件属性值的大小,构建对象的优势邻域,再由对象邻域的直觉模糊值确定对象的上下近似;根据对象的上下近似和不同对象的直觉模糊值确定对象间的区分关系,利用分辨矩阵给出知识约简和规则提取算法;最后将直觉模糊粗糙模型应用于政府IT项目绩效审计评价规则的获取,得到了较为合理的IT项目绩效评价规则.     

基于敏捷过程的IT项目范围管理的研究与应用

文中对项目范围管理和敏捷过程的基础理论、国内外发展趋势进行了研究,分析了项目范围管理和敏捷过程.利用敏捷过程应对IT项目范围变化,对原有的项目范围定义做出适应性改变,提出了改进的IT项目范围管理定义模式,并在此基础上提出了基于敏捷过程的IT项目范围管理模型.该模型结合敏捷核心思想,分析了敏捷需求建模的目标、价值观和原则.在遵循敏捷建模的价值观和原则的前提下,该模型要求:调整界定产品范围、调整界定工作范围、设计持续集成、交付验证产品,用以指导具体项目的范围管理实践. 以敏捷过程IT项目范围管理型作为理论基础,指导具体项目开发,将其应用于财务预算管理系统的设计开发中,解决了一直以来困扰项目组的大难题--项目范围变更的控制.通过敏捷过程IT项目范围管理模型在财务预算管理系统中的成功应用,充分验证了模型的实用性、合理性和正确性.研究建立敏捷过程IT项目范围管理建模的原则和实践,改善了项目范围管理,缩短了软件开发周期,降低了项目成本,提高了项目生产效率.     

东风的IT档位

IT与业务之间的关系,在东风汽车有限公司被看做如汽车驾驶过程中“油离配合”般的重要。何种IT项目会被通过审核,它会给企业带来何种收益,这些涉及到IT项目考核评估的方面,均被量化为各种测算体系。     

价值管理阐释IT治理

很少有企业在对业务系统的投入中获得了高额的商业回报。真正达到预期业务和管理目标的成功案例在中国的企业中并不普遍。问题在哪里呢? 追求IT投入收益最大化的IT治理得到了各个方面的关注。作者试图从IT治理中最为核心的价值管理的角度去解释IT治理的思想和流程在企业IT项目管理中的应用。对于任何一个企业IT项目,企业的决策者都要始终抓住两个问题:·如何保证我们的项目投资决策是正确的?·如何确保项目的每个环节都与企业的业务战略与业务目标相符合?一个从价值最大化角度管理IT项目的企业一般采用如下项目管理路线图, 在企业的软…     

广义人工生命在项目组合智能管理中的应用

为考虑IT项目组合智能管理中人的因素,在“广义人工生命”（GAL）的KVP（种类、活性、过程）模型框架下,将IT项目与生命系统进行类比。分析了IT项目组合智能管理系统中存在的广义人工生命性能,建立了IT项目的丝蚕模型框架、生命活性模型、生命历程模型。重新定义了组件IT项目,给出了评估IT项目生长发育健康度的方法以及在项目组合智能管理中的应用。     

我们需要IT治理吗？

IT治理的使命是保持IT与业务目标一致,推动业务发展,促使收益最大化,合理利用IT资源,适当管理与IT相关的风险。其目标将帮助管理层建立以组织战略为导向,以外界环境为依据,以业务与IT整合为中心的观念,正确定位IT部门在整个组织中的作用,这些IT治理的使命和目标的实现需要通过多种辅助手段来实现。目前国际上通行的标准主要有四个:COBIT、ITIL、ISO/IEC17799和PRINCE2。四个标准各有所长,COBIT着重IT控制和量度指标方面,ISO17799覆盖IT安全,而ITIL重点在于流程,PRINCE2则描述了一个项目如何被切分成一些可供管理的阶…     

基于优势-直觉模糊粗糙模型的IT项目绩效审计评价规则获取

针对我国政府审计机关对政府投资的I`I}项目进行绩效审计评价规则知识获取的困难,考虑了条件属性取值 为优势精确值、分类结果为直觉模糊值的决策系统规则获取问题。首先比较条件属性值的大小,构建对象的优势部 域,再由对象邻域的直觉模糊值确定对象的上下近似;根据对象的上下近似和不同对象的直觉模糊值确定对象间的区 分关系,利用分辫矩阵给出知识约简和规则提取算法;最后将直觉模糊粗糙模型应用于政府I"I'项目绩效审计评价规 则的获取,得到了较为合理的IT项目绩效评价规则。     

关注企业量化指标的改进

传统的项目投资评估方法不适合于IT项目投资效益的评估。IT投资的收益就蕴藏在企业量化指标的改进和完善中。     

A new type-2 fuzzy set of linguistic variables for the fuzzy analytic hierarchy process

The fuzzy analytic hierarchy process (FAHP) has been used to solve various multi-criteria decision-making problems where trapezoidal type-1 fuzzy sets are utilized in defining decision-makers’ linguistic judgment. Previous theories have suggested that interval type-2 fuzzy sets (IT2 FS) can offer an alternative that can handle vagueness and uncertainty. This paper proposes a new FAHP characterized by IT2 FS for linguistic variables. Differently from the typical FAHP, which directly utilizes trapezoidal type-1 fuzzy numbers, this method introduces IT2 FS to enhance judgment in the fuzzy decision-making environment. This new model includes linguistic variables in IT2 FS and a rank value method for normalizing upper and lower memberships of IT2 FS. The proposed model is illustrated by a numerical example of work safety evaluation. Comparable results are also presented to check the feasibility of the proposed method. It is shown that the ranking order of the proposed method is consistent with the other two methods despite difference in weight priorities.     

Static Output Feedback Control for Interval Type‐2 T‐S Fuzzy Systems Based on Fuzzy Lyapunov Functions

This study aims to design an interval type‐2 (IT2) fuzzy static output feedback controller to stabilize the IT2 Takagi‐Sugeno (T‐S) fuzzy system. Conservative results may be obtained when a common quadratic Lyapunov function is utilized to investigate the stability of T‐S fuzzy systems. A fuzzy Lyapunov function is employed in this study to analyze the stability of the IT2 fuzzy closed‐loop system formed by the IT2 T‐S fuzzy model and the IT2 fuzzy static output feedback controller. Stability conditions in the form of linear matrix inequalities are derived. Several slack matrices are introduced to further reduce the conservativeness of stability analysis. The membership‐function shape‐dependent analysis approach is also employed to relax the stability results. The numerical examples illustrate the effectiveness of the proposed conditions.     

A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks

In real life, information about the world is uncertain and imprecise. The cause of this uncertainty is due to: deficiencies on given information, the fuzzy nature of our perception of events and objects, and on the limitations of the models we use to explain the world. The development of new methods for dealing with information with uncertainty is crucial for solving real life problems. In this paper three interval type-2 fuzzy neural network (IT2FNN) architectures are proposed, with hybrid learning algorithm techniques (gradient descent backpropagation and gradient descent with adaptive learning rate backpropagation). At the antecedents layer, a interval type-2 fuzzy neuron (IT2FN) model is used, and in case of the consequents layer an interval type-1 fuzzy neuron model (IT1FN), in order to fuzzify the rule’s antecedents and consequents of an interval type-2 Takagi-Sugeno-Kang fuzzy inference system (IT2-TSK-FIS). IT2-TSK-FIS is integrated in an adaptive neural network, in order to take advantage the best of both models. This provides a high order intuitive mechanism for representing imperfect information by means of use of fuzzy If-Then rules, in addition to handling uncertainty and imprecision. On the other hand, neural networks are highly adaptable, with learning and generalization capabilities. Experimental results are divided in two kinds: in the first one a non-linear identification problem for control systems is simulated, here a comparative analysis of learning architectures IT2FNN and ANFIS is done. For the second kind, a non-linear Mackey-Glass chaotic time series prediction problem with uncertainty sources is studied. Finally, IT2FNN proved to be more efficient mechanism for modeling real-world problems.     

一类乘积型区间二型模糊控制器的解析结构

区间二型模糊控制器的降型算法需要使用迭代计算,是导致其解析结构推导困难的主要原因.针对乘积型区间二型模糊控制器,本文提出了一种新的解析结构推导方法.区间二型模糊控制器的配置为:三角形输入模糊集,一型输出模糊单值,集合中心法降型器,平均法解模糊器和基于乘积型"与"操作的规则前件.通过对比传统PID控制器的解析结构,证明了区间二型模糊控制器等效于两个PI(或PD)控制器之和.利用KM算法的迭代终止条件,提出了6步骤IC划分法,保证了激活子空间的正确划分.叠加各个子空间,即可得出全局IC划分图.为了避免重复求解符号数学方程,提出了IC边界线的直接定义法,改进了6步骤IC划分法的便利性.本文方法避开了降型算法的迭代计算,可以保证推导出区间二型模糊控制器的闭环解析表达式.     

Design of interval type-2 fuzzy sliding-mode controller

In this paper, an interval type-2 fuzzy sliding-mode controller (IT2FSMC) is proposed for linear and nonlinear systems. The proposed IT2FSMC is a combination of the interval type-2 fuzzy logic control (IT2FLC) and the sliding-mode control (SMC) which inherits the benefits of these two methods. The objective of the controller is to allow the system to move to the sliding surface and remain in on it so as to ensure the asymptotic stability of the closed-loop system. The Lyapunov stability method is adopted to verify the stability of the interval type-2 fuzzy sliding-mode controller system. The design procedure of the IT2FSMC is explored in detail. A typical second order linear interval system with 50% parameter variations, an inverted pendulum with variation of pole characteristics, and a Duffing forced oscillation with uncertainty and disturbance are adopted to illustrate the validity of the proposed method. The simulation results show that the IT2FSMC achieves the best tracking performance in comparison with the type-1 Fuzzy logic controller (T1FLC), the IT2FLC, and the type-1 fuzzy sliding-mode controller (T1FSMC).     

Bearing condition prediction considering uncertainty: An interval type-2 fuzzy neural network approach

Rolling-element bearings are critical components of rotating machinery. It is important to accurately predict in real-time the health condition of bearings so that maintenance practices can be scheduled to avoid malfunctions or even catastrophic failures. In this paper, an Interval Type-2 Fuzzy Neural Network (IT2FNN) is proposed to perform multi-step-ahead condition prediction of faulty bearings. Since the IT2FNN defines an interval type-2 fuzzy logic system in the form of a multi-layer neural network, it can integrate the merits of each, such as fuzzy reasoning to handle uncertainties and neural networks to learn from data. The interval type-2 fuzzy linguistic process in the IT2FNN enables the system to handle prediction uncertainties, since the type-2 fuzzy sets are such sets whose membership grades are type-1 fuzzy sets that can be used in failure prediction due to the difficult determination of an exact membership function for a fuzzy set. Noisy data of faulty bearings are used to validate the proposed predictor, whose performance is compared with that of a prevalent type-1 condition predictor called Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that better prediction accuracy can be achieved via the IT2FNN.     

Stability analysis and controller design of interval type-2 fuzzy systems with time delay

The type-2 fuzzy models can handle the system uncertainties directly based on the type-2 fuzzy sets. In this paper, the Takagi–Sugeno fuzzy model approach is extended to the stability analysis and controller design for interval type-2 (IT2) fuzzy systems with time-varying delay. Delay-dependent robust stability criteria are developed in terms of linear matrix inequalities by using the improvement technique of free-weighting matrices. Less conservative results are obtained by considering the information contained in the footprint of uncertainty. Finally, two simulation examples are presented to illustrate the effectiveness of the theoretical results. One is provided to show the merits of the proposed method, the other based on the continuous stirred tank reactor model is given to illustrate the design processes of IT2 fuzzy controller for a nonlinear system with parameter uncertainties.     

Conceptual design evaluation using interval type-2 fuzzy information axiom

Concept selection is the most critical part of the design process as it determines the direction of subsequent design stages. In addition, it is a difficult task because available information for decision-making at this stage is imprecise and subjective. This necessitates the need for fuzzy decision models for selecting the best conceptual design among a set of alternatives. Although ordinary fuzzy sets cover uncertainties of linguistic words to some extent, it is recommended to use interval type-2 fuzzy sets (IT2FS) to capture potential uncertainties of words. This paper presents a new concept selection methodology that extends the fuzzy information axiom (FIA) approach to incorporate IT2FSs. The proposed methodology is called interval-type-2 fuzzy information axiom (IT2-FIA). IT2-FIA method is also enriched by using ordered weighted geometric aggregation operator to include the decision maker's attitude during the aggregation process. A case study is given to demonstrate the potential of the methodology.