Showing papers on "Fuzzy logic published in 2007"

Global supplier development considering risk factors using fuzzy extended AHP-based approach

Abstract: Global supplier development is a multi-criterion decision problem which includes both qualitative and quantitative factors. The global supplier selection problem is more complex than domestic one and it needs more critical analysis. The aim of this paper is to identify and discuss some of the important and critical decision criteria including risk factors for the development of an efficient system for global supplier selection. Fuzzy extended analytic hierarchy process (FEAHP) based methodology will be discussed to tackle the different decision criteria like cost, quality, service performance and supplier's profile including the risk factors involved in the selection of global supplier in the current business scenario. FEAHP is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different decision variables. The linguistic level of comparisons produced by the customers and experts for each comparison are tapped in the form triangular fuzzy numbers to construct fuzzy pair-wise comparison matrices. The implementation of the system is demonstrated by a problem having four stages of hierarchy which contains different criteria and attributes at wider perspective. The proposed model can provide not only a framework for the organization to select the global supplier but also has the capability to deploy the organization's strategy to its supplier.

Type-2 fuzzy sets and systems: an overview

Abstract: This paper provides an introduction to and an overview of type-2 fuzzy sets (T2 FS) and systems. It does this by answering the following questions: What is a T2 FS and how is it different from a T1 FS? Is there new terminology for a T2 FS? Are there important representations of a T2 FS and, if so, why are they important? How and why are T2 FSs used in a rule-based system? What are the detailed computations for an interval T2 fuzzy logic system (IT2 FLS) and are they easy to understand? Is it possible to have an IT2 FLS without type reduction? How do we wrap this up and where can we go to learn more?

Interval Neutrosophic Sets and Logic: Theory and Applications in Computing

Abstract: A neutrosophic set is a part of neutrosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a powerful general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Here, we define the set-theoretic operators on an instance of a neutrosophic set, and call it an Interval Neutrosophic Set (INS). We prove various properties of INS, which are connected to operations and relations over INS. We also introduce a new logic system based on interval neutrosophic sets. We study the interval neutrosophic propositional calculus and interval neutrosophic predicate calculus. We also create a neutrosophic logic inference system based on interval neutrosophic logic. Under the framework of the interval neutrosophic set, we propose a data model based on the special case of the interval neutrosophic sets called Neutrosophic Data Model. This data model is the extension of fuzzy data model and paraconsistent data model. We generalize the set-theoretic operators and relation-theoretic operators of fuzzy relations and paraconsistent relations to neutrosophic relations. We propose the generalized SQL query constructs and tuple-relational calculus for Neutrosophic Data Model. We also design an architecture of Semantic Web Services agent based on the interval neutrosophic logic and do the simulation study.

Modeling Decisions: Information Fusion and Aggregation Operators

Abstract: Basic Notions.- to Functional Equations.- Synthesis of Judgements.- Fuzzy Measures.- From the Weighted Mean to Fuzzy Integrals.- Indices and Evaluation Methods.- Selection of the Model.

A fuzzy optimization model for QFD planning process using analytic network approach

Abstract: In both the quality improvement and the design of a product, the engineering characteristics affecting product performance are primarily identified and improved to optimize customer needs (CNs). Especially, the limited resources and increased market competition and product complexity require a customer-driven quality management and product development system achieving higher customer satisfaction. Quality function deployment (QFD) is used as a powerful tool for improving product design and quality, and procuring a customer-driven quality system. In this paper, an integrated framework based on fuzzy-QFD and a fuzzy optimization model is proposed to determine the product technical requirements (PTRs) to be considered in designing a product. The coefficients of the objective function are obtained from a fuzzy analytic network process (ANP) approach. Fuzzy analytic hierarchy process (AHP) is also used in the proposed framework. An application in a Turkish Company producing PVC window and door systems is presented to illustrate the proposed framework.

Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment

Abstract: This paper develops an evaluation approach based on the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), to help the Air Force Academy in Taiwan choose optimal initial training aircraft in a fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterised by triangular fuzzy numbers. This study applies the fuzzy multi-criteria decision-making (MCDM) method to determine the importance weights of evaluation criteria and to synthesize the ratings of candidate aircraft. Aggregated the evaluators' attitude toward preference; then TOPSIS is employed to obtain a crisp overall performance value for each alternative to make a final decision. This approach is demonstrated with a real case study involving 16 evaluation criteria, seven initial propeller-driven training aircraft assessed by 15 evaluators from the Taiwan Air Force Academy.

Group Decision-Making Model With Incomplete Fuzzy Preference Relations Based on Additive Consistency

Abstract: In decision-making problems there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem, and as a result they may present incomplete preferences, i.e., some preference values may not be given or may be missing. In this paper, we present a new model for group decision making in which experts' preferences can be expressed as incomplete fuzzy preference relations. As part of this decision model, we propose an iterative procedure to estimate the missing information in an expert's incomplete fuzzy preference relation. This procedure is guided by the additive-consistency (AC) property and only uses the preference values the expert provides. The AC property is also used to measure the level of consistency of the information provided by the experts and also to propose a new induced ordered weighted averaging (IOWA) operator, the AC-IOWA operator, which permits the aggregation of the experts' preferences in such a way that more importance is given to the most consistent ones. Finally, the selection of the solution set of alternatives according to the fuzzy majority of the experts is based on two quantifier-guided choice degrees: the dominance and the nondominance degree

Identification of overlapping community structure in complex networks using fuzzy c-means clustering

Abstract: Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, we devise a novel algorithm to identify overlapping communities in complex networks by the combination of a new modularity function based on generalizing NG's Q function, an approximation mapping of network nodes into Euclidean space and fuzzy c-means clustering. Experimental results indicate that the new algorithm is efficient at detecting both good clusterings and the appropriate number of clusters.

Logics of Organization Theory: Audiences, Codes, and Ecologies

Abstract: Building theories of organizations is challenging: theories are partial and "folk" categories are fuzzy. The commonly used tools--first-order logic and its foundational set theory--are ill-suited for handling these complications. Here, three leading authorities rethink organization theory. Logics of Organization Theory sets forth and applies a new language for theory building based on a nonmonotonic logic and fuzzy set theory. In doing so, not only does it mark a major advance in organizational theory, but it also draws lessons for theory building elsewhere in the social sciences. Organizational research typically analyzes organizations in categories such as "bank," "hospital," or "university." These categories have been treated as crisp analytical constructs designed by researchers. But sociologists increasingly view categories as constructed by audiences. This book builds on cognitive psychology and anthropology to develop an audience-based theory of organizational categories. It applies this framework and the new language of theory building to organizational ecology. It reconstructs and integrates four central theory fragments, and in so doing reveals unexpected connections and new insights.

FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data

Abstract: Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing clustering approaches, mainly developed in computer science, have been adapted to microarray data analysis. However, previous studies revealed that microarray datasets have very diverse structures, some of which may not be correctly captured by current clustering methods. We therefore approached the problem from a new starting point, and developed a clustering algorithm designed to capture dataset-specific structures at the beginning of the process. The clustering algorithm is named Fuzzy clustering by Local Approximation of MEmbership (FLAME). Distinctive elements of FLAME are: (i) definition of the neighborhood of each object (gene or sample) and identification of objects with "archetypal" features named Cluster Supporting Objects, around which to construct the clusters; (ii) assignment to each object of a fuzzy membership vector approximated from the memberships of its neighboring objects, by an iterative converging process in which membership spreads from the Cluster Supporting Objects through their neighbors. Comparative analysis with K-means, hierarchical, fuzzy C-means and fuzzy self-organizing maps (SOM) showed that data partitions generated by FLAME are not superimposable to those of other methods and, although different types of datasets are better partitioned by different algorithms, FLAME displays the best overall performance. FLAME is implemented, together with all the above-mentioned algorithms, in a C++ software with graphical interface for Linux and Windows, capable of handling very large datasets, named Gene Expression Data Analysis Studio (GEDAS), freely available under GNU General Public License. The FLAME algorithm has intrinsic advantages, such as the ability to capture non-linear relationships and non-globular clusters, the automated definition of the number of clusters, and the identification of cluster outliers, i.e. genes that are not assigned to any cluster. As a result, clusters are more internally homogeneous and more diverse from each other, and provide better partitioning of biological functions. The clustering algorithm can be easily extended to applications different from gene expression analysis.

Type-2 FLCs: A New Generation of Fuzzy Controllers

Abstract: Type-1 fuzzy logic controllers (FLCs) have been applied to date with great success to many different applications. However, for dynamic unstructured environments and many real-world applications, there is a need to cope with large amounts of uncertainties. The traditional type-1 FLC using crisp type-1 fuzzy sets cannot directly handle such uncertainties. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce a better performance. Hence, type-2 FLCs will have the potential to overcome the limitations of type-1 FLCs and produce a new generation of fuzzy controllers with improved performance for many applications, which require handling high levels of uncertainty. This paper introduces briefly the interval type-2 FLC and its benefits. We also present briefly the type-2 FLC application to three challenging domains: industrial control, mobile robots control and ambient intelligent environments control

Impact of Network Reconfiguration on Loss Allocation of Radial Distribution Systems

Abstract: This paper presents allocation of power losses to consumers connected to radial distribution networks before and after network reconfiguration in a deregulated environment. Loss allocation is made in a quadratic way and it is based on identifying the real and imaginary parts of current in each branch, and losses are allocated to consumers. The network reconfiguration algorithm is based on the fuzzy multiobjective approach and the max-min principle is adopted for the multiobjective optimization in a fuzzy framework. Multiple objectives are considered for real-power loss reduction in which nodes voltage deviation is kept within a range, and an absolute value of branch currents is not allowed to exceed their rated capacities. At the same time, a radial network structure is maintained with all loads energized. The three objectives considered are modeled with fuzzy sets to evaluate their imprecise nature and one can provide his or her anticipated value of each objective. A 69-node example is considered to demonstrate the effectiveness of the proposed method.

Fuzzy-Rough Sets Assisted Attribute Selection

Abstract: Attribute selection (AS) refers to the problem of selecting those input attributes or features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, attribute selectors preserve the original meaning of the attributes after reduction. This has found application in tasks that involve datasets containing huge numbers of attributes (in the order of tens of thousands) which, for some learning algorithms, might be impossible to process further. Recent examples include text processing and web content classification. AS techniques have also been applied to small and medium-sized datasets in order to locate the most informative attributes for later use. One of the many successful applications of rough set theory has been to this area. The rough set ideology of using only the supplied data and no other information has many benefits in AS, where most other methods require supplementary knowledge. However, the main limitation of rough set-based attribute selection in the literature is the restrictive requirement that all data is discrete. In classical rough set theory, it is not possible to consider real-valued or noisy data. This paper investigates a novel approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses these problems and retains dataset semantics. FRFS is applied to two challenging domains where a feature reducing step is important; namely, web content classification and complex systems monitoring. The utility of this approach is demonstrated and is compared empirically with several dimensionality reducers. In the experimental studies, FRFS is shown to equal or improve classification accuracy when compared to the results from unreduced data. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. In addition, it is shown that FRFS is more powerful than the other AS techniques in the comparative study

Knowledge-Based Clustering: From Data to Information Granules

Abstract: ForewordPreface1 Clustering and Fuzzy Clustering1 Introduction2 Basic Notions and Notation21 Types of Data22 Distance and Similarity3 Main Categories of Clustering Algorithms31 Hierarchical Clustering32 Objective Function - Based Clustering4 Clustering and Classification5 Fuzzy Clustering6 Cluster Validity7 Extensions of Objective Function-Based Fuzzy Clustering71 Augmented Geometry of Fuzzy Clusters: Fuzzy C-Varieties72 Possibilistic Clustering73 Noise Clustering8 Self Organizing Maps and Fuzzy Objective Function Based Clustering9 ConclusionsReferences2 Computing with Granular Information: Fuzzy Sets and Fuzzy Relations1 A Paradigm of Granular Computing: Information Granules and their Processing2 Fuzzy Sets as Human-Centric Information Granules3 Operations on Fuzzy Sets4 Fuzzy Relations5 Comparison of Two Fuzzy Sets6 Generalizations of Fuzzy Sets7 Shadowed Sets8 Rough Sets9 Granular Computing and Distributed Processing10 ConclusionsReferences3 Logic-Oriented Neurocomputing1 Introduction2 Main Categories of Fuzzy Neurons21 Aggregative Neurons22 Referential (reference) Neurons3 Architectures of Logic Networks4 Interpretation Aspects of the Networks5 The Granular Interfaces of Logic Processing6 ConclusionsReferences4 Conditional Fuzzy Clustering1 Introduction2 Problem Statement: Context Fuzzy Sets and Objective Function3 The Optimization Problem4 Computational Considerations of Conditional Clustering5 Generalizations of the Algorithm Through the Aggregation Operator6 Fuzzy Clustering with Spatial Constraints7 ConclusionsReferences5 Clustering with Partial Supervision1 Introduction2 Problem Formulation3 The Design of the Clusters4 Experimental Examples5 Cluster-Based Tracking Problem6 ConclusionsReferences6 Principles of Knowledge-Based Guidance in Fuzzy Clustering1 Introduction2 Examples of Knowledge-Oriented Hints and their General Taxonomy3 The Optimization Environment of Knowledge-Enhanced Clustering4 Quantification of Knowledge-Based Guidance Hints and Their Optimization5 The Organization of the Interaction Process6 Proximity - Based Clustering (P-FCM)7 Web Exploration and P-FCM8 Linguistic Augmentation of Knowledge-Based Hints9 Concluding CommentsReferences7 Collaborative Clustering1 Introduction and Rationale2 Horizontal and Vertical Clustering3 Horizontal Collaborative Clustering31 Optimization Details32 The Flow of Computing of Collaborative Clustering33 Quantification of the Collaborative Phenomenon of the Clustering4 Experimental Studies5 Further Enhancements of Horizontal Clustering6 The Algorithm of Vertical Clustering7 A Grid Model of Horizontal and Vertical Clustering8 Consensus Clustering9 ConclusionsReferences8 Directional Clustering1 Introduction2 Problem Formulation21 The Objective Function22 The Logic Transformation Between Information Granules3 The Algorithm4 The Overall Development Framework of Directional Clustering5 Numerical Studies6 ConclusionsReferences9 Fuzzy Relational Clustering1 Introduction and Problem Statement2 FCM for Relational Data3 Decomposition of Fuzzy Relational Patterns31 Gradient-Based Solution to the Decomposition Problem32 Neural Network Model of the Decomposition Problem4 Comparative Analysis5 ConclusionsReferences10 Fuzzy Clustering of Heterogeneous Patterns1 Introduction2 Heterogeneous Data3 Parametric Models of Granular Data4 Parametric Mode of Heterogeneous Fuzzy Clustering5 Nonparametric Heterogeneous Clustering51 A Frame of Reference52 Representation of Granular Data Through the Possibility-Necessity Transformation53 Dereferencing6 ConclusionsReferences11 Hyperbox Models of Granular Data: The Tchebyschev FCM1 Introduction2 Problem Formulation3 The Clustering Algorithm-Detailed Considerations4 The Development of Granular Prototypes5 The Geometry of Information Granules6 Granular Data Description: A General Model7 ConclusionsReferences12 Genetic Tolerance Fuzzy Neural Networks1 Introduction2 Operations of Thresholdings and Tolerance: Fuzzy Logic-Based Generalizations3 The Topology of the Logic Network4 Genetic Optimization5 Illustrative Numeric Studies6 ConclusionsReferences13 Granular Prototyping1 Introduction2 Problem Formulation21 Expressing Similarity Between Two Fuzzy Sets22 Performance Index (objective function)3 Prototype Optimization4 The Development of Granular Prototypes41 Optimization of the Similarity Levels42 An Inverse Similarity Problem5 ConclusionsReferences14 Granular Mappings1 Introduction and Problem Statement2 Possibility and Necessity measure as the Computational Vehicle of Granular Representation3 Building the Granular Mapping4 The Design of Multivariable Granular Mappings Through Fuzzy Clustering5 Quantification of Granular Mappings6 Experimental Studies7 ConclusionsReferences15 Linguistic Modeling1 Introduction2 The Cluster-Based Representation of the Input - Output Mapping3 Conditional Clustering in the development of a blueprint of granular models4 Granular neuron as a Generic Processing Element in Granular Networks5 The Architecture of Linguistic Models Based on Conditional Fuzzy Clustering6 Refinements of Linguistic Models7 ConclusionsReferencesBibliographyIndex

Generalizing TOPSIS for fuzzy multiple-criteria group decision-making

Abstract: In this paper, we generalize TOPSIS to fuzzy multiple-criteria group decision-making (FMCGDM) in a fuzzy environment. TOPSIS is one of the well-known methods for multiple-criteria decision-making (MCDM). Most of the steps of TOPSIS can be easily generalized to a fuzzy environment, except max and min operations in finding the ideal solution and negative ideal solution. Thus we propose two operators Up and Lo which satisfy the partial ordering relation on fuzzy numbers to the generalization of TOPSIS. In generalized TOPSIS, these two operations (Up and Lo) are employed to find ideal and negative ideal solutions under a fuzzy environment. Then the FMCGDM problem can be solved effectively and efficiently.

A Survey on Fuzzy Implication Functions

Abstract: One of the key operations in fuzzy logic and approximate reasoning is the fuzzy implication, which is usually performed by a binary operator, called an implication function or, simply, an implication. Many fuzzy rule based systems do their inference processes through these operators that also take charge of the propagation of uncertainty in fuzzy reasonings. Moreover, they have proved to be useful also in other fields like composition of fuzzy relations, fuzzy relational equations, fuzzy mathematical morphology, and image processing. This paper aims to present an overview on fuzzy implication functions that usually are constructed from t-norms and t-conorms but also from other kinds of aggregation operators. The four most usual ways to define these implications are recalled and their characteristic properties stated, not only in the case of [0,1] but also in the discrete case.

A Descriptor System Approach to Fuzzy Control System Design via Fuzzy Lyapunov Functions

Abstract: There has been a flurry of research activities in the analysis and design of fuzzy control systems based on linear matrix inequalities (LMIs). This paper presents a descriptor system approach to fuzzy control system design using fuzzy Lyapunov functions. The design conditions are still cast in terms of LMIs but the proposed approach takes advantage of the redundancy of descriptor systems to reduce the number of LMI conditions which leads to less computational requirement. To obtain relaxed LMI conditions, new types of fuzzy controller and fuzzy Lyapunov function are proposed. A salient feature of the LMI conditions derived in this paper is to relate the feasibility of the LMIs to the switching speed of each linear subsystem (to be exact, to the lower bounds of time derivatives of membership functions). To illustrate the validity and applicability of the proposed approach, two design examples are provided. The first example shows that the LMI conditions based on the fuzzy Lyapunov function are less conservative than those based on a common (standard) Lyapunov function. The second example illustrates the utility of the fuzzy Lyapunov function approach in comparison with a piecewise Lyapunov function approach.

Fuzzy Multi-Level Security: An Experiment on Quantified Risk-Adaptive Access Control

Abstract: This paper presents a new model for, or rather a new way of thinking about adaptive, risk-based access control. Our basic premise is that there is always inherent uncertainty and risk in access control decisions that is best addressed in an explicit way. We illustrate this concept by showing how the rationale of the well-known, Bell-Lapadula model based, multi-level security (MLS) access control model could be used to develop a risk-adaptive access control model. This new model is more like a fuzzy logic control system than a traditional access control system and hence the name "fuzzy MLS". The long version of this paper is published as an IBM Research Report.

Geometric Type-1 and Type-2 Fuzzy Logic Systems

Abstract: This paper presents a novel approach to the representation of type-1 and type-2 fuzzy sets utilising computational geometry. To achieve this our approach borrows ideas from the field of computational geometry and applies these techniques in the novel setting of fuzzy logic. We provide new algorithms for various operations on type-1 and type-2 fuzzy sets and for defuzzification. Results of experiments indicate that this approach reduces the execution speed of these operations

Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions (Mendel, J.M.; 2001) [book review]

Abstract: The book comprises 14 chapters and three appendices. The chapters are organized onto four parts: Preliminaries, Type-1 Fuzzy Logic Systems, Type-2 Fuzzy sets, and Type-2 Fuzzy Logic Systems. The book proves to be a valuable resource for professionals seeking to work with fuzzy sets in general and type-2 fuzzy sets in particular.

Type-2 Fuzzy Sets and Systems: An Overview [corrected reprint]

Abstract: As originally published in the February 2007 issue of IEEE Computational Intelligence Magazine, the above titled paper (ibid., vol. 2, no. 1, pp. 20-29, Feb 07) contained errors in mathematics that were introduced by the publisher. The corrected version is reprinted in its entirety.