COTS selection using fuzzy interactive approach

In this paper, we introduce fuzzy mathematical programming (FMP) for decision-making related to software creation by selecting optimal commercial-off-the-shelf (COTS) products in a modular software system. Each module in such software systems have different alternatives with variations in their properties, for example, quality, reliability, execution time, size and cost. Due to these variations, component-based software developers generally deals with the problem of selecting appropriate COTS products. The development of COTS-based systems largely depends on the success of the selection process. Various crisp optimization models of COTS products selection have been proposed in literature. However, in real COTS products selection problem, it is difficult to estimate precisely the values of various model parameters due to lack of sufficient data and also because of measurement errors. Hence, instead of crisp optimization model, if we use flexible optimization model then we might obtain results which are more preferred by the decision maker. In this study, we use multiple methodologies such as quality model, analytical hierarchy process and FMP to develop fuzzy multiobjective optimization model of the COTS products selection. To determine a preferred compromise solution for the multiobjective optimization problem, an interactive fuzzy approach is used.     

Intuitionistic Fuzzy Logic Control for Heater Fans

The concept of intuitionistic fuzzy systems, including intuitionistic fuzzy sets and intuitionistic fuzzy logic, was introduced by Atanassov as a generalization of fuzzy systems. Intuitionistic fuzzy systems provide a mechanism for communication between computing systems and humans. In this paper, we describe the development of an intuitionistic fuzzy logic controller for heater fans, developed on the basis of intuitionistic fuzzy systems. Intuitionistic fuzzy inference systems and defuzzification techniques are used to obtain crisp output (i.e., speed of the heater fan) from an intuitionistic fuzzy input (i.e., ambient temperature). The speed of the heater fan is calculated using intuitionistic fuzzy rules applied in an inference engine using defuzzification methods.     

Fuzzy dynamical systems

A fuzzy dynamical system on an underlying complete, locally compact metric state space X is defined axiomatically in terms of a fuzzy attainability set mapping on X. This definition includes as special cases crisp single and multivalued dynamical systems on X. It is shown that the support of such a fuzzy dynamical system on X is a crisp multivalued dynamical system on X, and that such a fuzzy dynamical system can be considered as a crisp dynamical system on a state space of nonempty compact fuzzy subsets of X. In addition fuzzy trajectories are defined, their existence established and various properties investigated.     

Deriving crisp and consistent priorities for fuzzy AHP-based multicriteria systems using non-linear constrained optimization

Fuzzy optimization models are used to derive crisp weights (priority vectors) for the fuzzy analytic hierarchy process (AHP) based multicriteria decision making systems. These optimization models deal with the imprecise judgements of decision makers by formulating the optimization problem as the system of constrained non linear equations. Firstly, a Genetic Algorithm based heuristic solution for this optimization problem is implemented in this paper. It has been found that the crisp weights derived from this solution for fuzzy-AHP system, sometimes lead to less consistent or inconsistent solutions. To deal with this problem, we have proposed a consistency based constraint for the optimization models. A decision maker can set the consistency threshold value and if the solution exists for that threshold value then crisp weights can be derived, otherwise it can be concluded that the fuzzy comparison matrix for AHP is not consistent for the given threshold. Three examples are considered to demonstrate the effectiveness of the proposed method. Results with the proposed constraint based fuzzy optimization model are more consistent than the existing optimization models.     

基于三角模糊集划分的MISO Mamdani模糊系统参数关联性分析

基于三角模糊集划分的MISO Mamdani模糊系统是模糊规则获取研究领域的一类重点研究对象。在这种划分下，系统的输入空间被划分成若干个超立方体，在每个超立方体内系统的输出与2^p条模糊规则中的参数相关。本文将每个超立方体分成若干个区域，并且严格论证了在每个区域内系统输出仅与其中2^p-1条模糊规则及另2^p-1条模糊规则中相应参数平均值相关。这个结论使得可以根据样本误差的区域分布情况优化参数的选取。对于该类系统的参数优化过程中的参数选取具有积极的意义。     

Topological relations between fuzzy regions: derivation of verbal terms

A method is presented to generate verbal terms about topological relations between fuzzy regions. The methodology relies on the fuzzy 4-intersection, which is a generalization of the crisp 4-intersection of Egenhofer and co-workers. The computation of the similarity between the fuzzy- and the crisp 4-intersection enables the verbal term, i.e., the linguistic variable, to be derived. The linguistic variable contains a semantic part which gives an immediate association to a crisp relation and a quantifier which indicates the strength of the relationship. Since the derivation of the linguistic variable depends on the definition of the boundary of the fuzzy regions, a method is presented to compute fuzzy boundaries. The approach here defines fuzzy boundaries so that each point in the fuzzy region is associated a partial membership in both the interior and the boundary of the region. This view is different from the boundary definition in crisp topology, but it agrees with the fuzzy set idea that elements can have partial membership in different sets. A simulation experiment demonstrates the properties of the proposed methodology, and it shows how the linguistic variable relates to an inclusion index. An example illustrates how some level of action can be associated to the linguistic variable, which is applicable in the course control of moving crafts, in military applications or in other kinds of operations where the level of warning or action depends on the topological relation between the fuzzy regions.The findings in this article are applicable to geographical information systems (GIS), the modelling of objects with indeterminate boundaries, in the reasoning about relations between geographical objects, or the evaluation of database queries. If the ideas in the present article are implemented in GIS, this will provide an enhanced user interface compared to most GIS today.     

Parametric nonlinear programming approach to fuzzy queues with bulk service

This paper proposes a procedure to construct the membership functions of the performance measures in bulk service queuing systems with the arrival rate and service rate are fuzzy numbers. The basic idea is to transform a fuzzy queue with bulk service to a family of conventional crisp queues with bulk service by applying the α-cut approach. On the basis of α-cut representation and the extension principle, a pair of parametric nonlinear programs is formulated to describe that family of crisp bulk service queues, via which the membership functions of the performance measures are derived. To demonstrate the validity of the proposed procedure, two fuzzy queues often encountered in transportation management are exemplified. Since the performance measures are expressed by membership functions rather than by crisp values, they completely conserve the fuzziness of input information when some data of bulk-service queuing systems are ambiguous. Thus the proposed approach for vague systems can represent the system more accurately, and more information is provided for designing queuing systems in real life. By extending to fuzzy environment, the bulk service queuing models would have wider applications.     

A soft computing-based approach to spatio-temporal prediction

This paper aims to incorporate intelligent mechanisms based on Soft Computing in Geographical Information Systems (GIS). The proposal here is to present a spatio-temporal prediction method of forestry evolution for a sequence of binary images by means of fuzzy inference systems (FIS), genetic algorithm (GA) and genetic programming (GP). The main inference is based on a fuzzy system which processes a set of crisp/fuzzy relations and infers a crisp relation representing the predicted image at a predefined date. The fuzzy system is formed by a fixed fuzzy rule base and a partition set that may be defined by an expert or optimized by means of a GA. Genetic programming may also be adopted to generate the size of predicted area used in the final stage of the inference process. The developed methodology is applied in regions of Venezuela, France and Guatemala to identify their forestry evolution trends. The proposed approaches are compared with other techniques to validate the system.     

Fuzzy control with limited control opportunities and response delay––a production-inventory control scenario

This paper examines the utility of fuzzy control over crisp in situations where the control opportunities are limited and the system response to control actions is delayed. Such situations are often encountered in production systems where limited resources restrict the control opportunities and the operation time delays the response. The performance of a real-time production-inventory control system is studied with fuzzy control strategy and compared with a corresponding crisp control and no-control strategy. The system consists of a production shop having a number of identical processing machines which produce two products. The output goes into two bins whose inventory is required to be controlled at desired level by varying the number of machines allocated to the products. Real-time inventory variation, output, average inventory and machine usage, number of setups and stock-outs are used as performance measures. The simulation results of the system with various configurations show that the capability of fuzzy control is seriously inhibited by limited opportunities and response delay although fuzzy has distinct advantage over crisp. As control opportunities increase fuzzy control becomes increasingly efficient with diminishing effect of response delay.     

Robust design of rule-based fuzzy controllers

This paper deals with the design of stable and robust rule-based fuzzy control systems. New expressions to compute indices which provide a measure of the stability and robustness of fuzzy control systems are presented. The relation between the modification of the rules and the stability is studied through the so-called sensitivity indices. The paper presents procedures that make use of these indices to improve the design of fuzzy control systems, including the modification of the rules to obtain the global stability of an unstable system with multiple attractors, and to improve the dynamic behavior or the robustness of a non-linear plant. An example with a fuzzy controller for a system with non-linear damping and saturation in the actuation is presented to illustrate the design procedure.     

Simulation of ECB decisions and forecast of short term Euro rate with an adaptive fuzzy expert system

The elaboration of optimal monetary policy strategies, and the statistical estimation of monetary policy rules followed by European Central Bank (ECB) in the new currency area of the Euro, are difficult to follow with the standard statistical models. For this reason we have developed an adaptive fuzzy expert system in order to mimic the framework on which the monetary policy strategy of the ECB is based. The expert system knowledge base consists of a set of fuzzy and crisp rules located at two different hierarchical levels. The high-level of the system receives some intermediate output values from the low-level and processes this information by means of a set of crisp rules. The low-level prepares these intermediate output values with the use of a fuzzy inference engine applied to economic input variables. The use of an expert system allows for modelling the ECB behaviour with the use of wider scope of knowledge, when compared with more traditional computational techniques. Rules at different hierarchical levels and at different intra-level groups, allow for managing the potentially contradictory structure of the ECB strategy. The system has been tested on the economic and financial time series going from the January 1999 to September 2000. The system’s correct prediction was estimated to overall 70% and, considering the complexity of the task, the results obtained are promising.     

An integrated approach for deriving priorities in analytic network process

A multiple objective programming approach for the analytic network process (ANP) is proposed to obtain all local priorities for crisp or interval judgments at one time, even in an inconsistent situation. The weakness of the ANP and fuzzy ANP (FANP) is that the complexity of generating priorities is equal to the number of comparison matrices. In the proposed approach, all sets of crisp priorities for each pairwise comparison matrix can be obtained directly. Moreover, from the outcomes of three examples, the power to reach a limiting supermatrix is less than or equal to the power of the FANP. Thus, the proposed approach can be regarded as an efficient alternative of the fuzzy ANP.     

Solving a full fuzzy linear programming using lexicography method and fuzzy approximate solution

This paper discusses full fuzzy linear programming (FFLP) problems of which all parameters and variable are triangular fuzzy numbers. We use the concept of the symmetric triangular fuzzy number and introduce an approach to defuzzify a general fuzzy quantity. For such a problem, first, the fuzzy triangular number is approximated to its nearest symmetric triangular number, with the assumption that all decision variables are symmetric triangular. An optimal solution to the above-mentioned problem is a symmetric fuzzy solution. Every FLP models turned into two crisp complex linear problems; first a problem is designed in which the center objective value will be calculated and since the center of a fuzzy number is preferred to (its) margin. With a special ranking on fuzzy numbers, the FFLP transform to multi objective linear programming (MOLP) where all variables and parameters are crisp.     

Homomorphic solution of fully fuzzy linear systems

In this paper, a fully fuzzy linear system (FFLS) is considered. By defuzzifying, the (n × n) FFLS can be replaced by three (n × n) crisp linear systems, and consequently its homomorphic solution in canonical trapezoidal form based on three (n × n) crisp linear solutions associated with three parameters, value, ambiguity, and fuzziness, is calculated.     

Solution to fuzzy system of linear equations with crisp coefficients

This paper presents a new and simple method to solve fuzzy real system of linear equations by solving two n × n crisp systems of linear equations. In an original system, the coefficient matrix is considered as real crisp, whereas an unknown variable vector and right hand side vector are considered as fuzzy. The general system is initially solved by adding and subtracting the left and right bounds of the vectors respectively. Then obtained solutions are used to get a final solution of the original system. The proposed method is used to solve five example problems. The results obtained are also compared with the known solutions and found to be in good agreement with them.     

A dominance intuitionistic fuzzy-rough set approach and its applications

Although the rough set and intuitionistic fuzzy set both capture the same notion, imprecision, studies on the combination of these two theories are rare. Rule extraction is an important task in a type of decision systems where condition attributes are taken as intuitionistic fuzzy values and those of decision attribute are crisp ones. To address this issue, this paper makes a contribution of the following aspects. First, a ranking method is introduced to construct the neighborhood of every object that is determined by intuitionistic fuzzy values of condition attributes. Moreover, an original notion, dominance intuitionistic fuzzy decision tables (DIFDT), is proposed in this paper. Second, a lower/upper approximation set of an object and crisp classes that are confirmed by decision attributes is ascertained by comparing the relation between them. Third, making use of the discernibility matrix and discernibility function, a lower and upper approximation reduction and rule extraction algorithm is devised to acquire knowledge from existing dominance intuitionistic fuzzy decision tables. Finally, the presented model and algorithms are applied to audit risk judgment on information system security auditing risk judgement for CISA, candidate global supplier selection in a manufacturing company, and cars classification.     

同一水平下模糊规则信息熵的描述

给出了模糊知识系统及模糊决策逻辑公式的定义,在此基础上描述了模糊决策逻辑公式及模糊知识系统下模糊规则的信息熵,讨论了模糊规则信息熵的相关性质;其次,利用模糊规则信息熵对模糊规则进行了分类、评价,从而为建立合理的模糊系统提供了一种有效的判定方法;最后,通过实例验证了所提出理论的正确性.     

A new method for ranking fuzzy numbers and its application to group decision making

In this paper, a new method for comparing fuzzy numbers based on a fuzzy probabilistic preference relation is introduced. The ranking order of fuzzy numbers with the weighted confidence level is derived from the pairwise comparison matrix based on 0.5-transitivity of the fuzzy probabilistic preference relation. The main difference between the proposed method and existing ones is that the comparison result between two fuzzy numbers is expressed as a fuzzy set instead of a crisp one. As such, the ranking order of n fuzzy numbers provides more information on the uncertainty level of the comparison. Illustrated by comparative examples, the proposed method overcomes certain unreasonable (due to the violation of the inequality properties) and indiscriminative problems exhibited by some existing methods. More importantly, the proposed method is able to provide decision makers with the probability of making errors when a crisp ranking order is obtained. The proposed method is also able to provide a probability-based explanation for conflicts among the comparison results provided by some existing methods using a proper ranking order, which ensures that ties of alternatives can be broken.     

一类大系统模糊随机多目标决策的建模与求解方法

本文提出和建立了一类大系统模糊随机多目标决策（ＦＳＭＯＤＭ）模型，讨论了模糊事件的模糊概率的若干基本性质．在此基础上，将ＦＳＭＯＤＭ模型转化为清晰的多目标决策模型，并给出了递阶分解协调求解方法．把该模型应用到大连市水资源与经济发展规划研究中取得了令人满意的结果．