Area Of Fuzzy Logic Research Articles

Possibility operators over n-valued Gödel logic

In the area of fuzzy logic, expansions of these logics by $\Delta$ operator have been intensively studied; the interest of $\Delta$ operator is due to the fact that it presents a fuzzy behavior, the associated systems were studied in propositional and first-order level. On the other hand, the possibility operators that define Łukasiewicz-Moisil algebras have been studied over different classes of algebras; these operators are known as Moisil's operators in the literature. One of these operators coincides with $\Delta$, showing there are other operators with fuzzy behavior. In this paper, we present the study of Moisil's operators over an extension of a fuzzy logic; namely, n-valued Gödel logic, thus opening the possibility to explore more fuzzy operators.

Symmetric I* Restriction Method of Fuzzy Inference

As one of important parts of fuzzy logic, fuzzy inference plays a vital role in the fields of fuzzy control, artificial intelligence, affective computing, image processing and so forth. Two key problems of fuzzy inference are FMP (fuzzy modus ponens) and FMT (fuzzy modus tollens). How to get the ideal solution for FMP and FMT is a difficult problem in the area of fuzzy logic. Aiming at such problem, from the idea of symmetric implicational reasoning, triple I* method and restriction theory, we put forward and investigate the α-symmetric I* restriction method, and then generalize it to the α(x,y)-symmetric I* restriction method. To begin with, the α-symmetric I* restriction principle and the α(x,y)-symmetric I* restriction principle are established. Furthermore, the equivalent condition to let a basic restriction solution exist is given. Then the unified solutions of the α-symmetric I* restriction method and the α(x,y)-symmetric I* restriction method are achieved for R-implications and (S, N)-implications. Besides, some special cases of optimal solutions are shown. Finally, the corresponding conclusions are provided when the two methods degenerate into the α-triple I* restriction method and α(x,y)-triple I* restriction method. These research results would be an important improvement for the fields of fuzzy inference, fuzzy logic and related applications.

Fuzzy logic and self-referential reasoning: a comparative study with some new concepts

In this paper, we consider metacognition and self-reference realized in the framework of fuzzy computing. Different aspects of self-reference are discussed from the viewpoint of logic, linguistics, and artificial intelligence. Previous work in the area of fuzzy logic is also revisited in this context. It is shown that the reasoning based on the truth qualification principle requires some modifications so that it naturally gives rise to type-2 fuzzy sets, in order to capture the uncertainty about selection of a solution for a set of self-referential statements.

Computing the Lattice of All Fixpoints of a Fuzzy Closure Operator

We present a fast bottom-up algorithm to compute all fixpoints of a fuzzy closure operator in a finite set over a finite chain of truth degrees, along with the partial order on the set of all fixpoints. Fuzzy closure operators appear in several areas of fuzzy logic and its applications, including formal concept analysis (FCA) that we use as a reference area of application in this paper. Several problems in FCA, such as computing all formal concepts from data with graded attributes or computing non-redundant bases of all attribute dependencies, can be reduced to the problem of computing fixpoints of particular fuzzy closure operators associated with the input data. The development of a general algorithm that is applicable, in particular, to these problems is the ultimate purpose of this paper. We present the algorithm, its theoretical foundations, and experimental evaluation.

Soft computing for crisis management and political decision making: the use of genetically evolved fuzzy cognitive maps

This paper examines the use of fuzzy cognitive maps (FCMs) as a technique for modeling political and strategic issues situations and supporting the decision-making process in view of an imminent crisis. Its object domain is soft computing using as its basic elements different methods from the areas of fuzzy logic, cognitive maps, neural networks and genetic algorithms. FCMs, more specifically, use notions borrowed from artificial intelligence and combine characteristics of both fuzzy logic and neural networks, in the form of dynamic models that describe a given political setting. The present work proposes the use of the genetically evolved certainty neuron fuzzy cognitive map (GECNFCM) as an extension of certainty neuron fuzzy cognitive maps (CNFCMs) aiming at overcoming the main weaknesses of the latter, namely the recalculation of the weights corresponding to each concept every time a new strategy is adopted. This novel technique combines CNFCMs with genetic algorithms (GAs), the advantage of which lies with their ability to offer the optimal solution without a problem-solving strategy, once the requirements are defined. Using a multiple scenario analysis we demonstrate the value of such a hybrid technique in the context of a model that reflects the political and strategic complexity of the Cyprus issue, as well as the uncertainties involved in it. The issue has been treated on a purely technical level, with distances carefully kept concerning all sides involved in it.

A preliminary study of the factors affecting the cost escalation of construction projects

An interview survey of Alberta-based construction contractors was conducted in July and August 1998. The purpose of this survey was to elicit information on the factors that cause unanticipated project cost escalation during construction, from the contractor's perspective. This paper presents the findings of this survey and a proposed method of modeling the factors identified. The majority of factors identified impact labour productivity, which is a major source of cost overruns. A combination of subjective, objective, and secondary indicators are used to measure these factors and to assess their impact on project performance. The main conclusion of this survey is that many of the factors affecting the cost of construction are evaluated in subjective and imprecise terms and are difficult to quantify. This paper presents a method of modeling these factors using fuzzy membership functions, which capture the imprecision and subjectivity associated with the measurement of these factors. It discusses a basis for the definition of these membership functions and a method of calibrating these functions to make them more widely applicable to suit different contexts. These membership functions are being incorporated in a set of expert rules, which reason about the factors affecting costs, their impact on the project, and the appropriate corrective actions. These expert rules are being developed as part of a fuzzy expert system for construction project monitoring and control. A method of calibrating membership functions to suit individual contexts is currently being developed, which would be a significant advancement in the area of fuzzy logic. Key words: construction, costs, expert systems, fuzzy logic, project control, survey.

Neuro-Fuzzy System for Robotics Applications

Over the last two decades several advances have been made in the areas of fuzzy logic and artificial neural networks. It is interesting to note that fuzzy logic and neural networks complement each other, and their fusion provides the benefits of both the technologies. Neural networks can deal with imprecise data and ill-defined activities; thus, they offer low-level computational features. On the other hand, fuzzy logic provides higher-level cognitive features as it can deal with issues such as approximate reasoning and natural language processing. The merging of these two fields results in an emerging paradigm called ‘neuro-fuzzy systems’. These are believed to have considerable potential in the areas of robotics, expert systems, medical diagnosis, control systems, pattern recognition and system modeling. The intent of this paper is to describe a Neuro-Fuzzy System (NFS) for on-line computation of inverse kinematic transformations of a robot manipulator. The NFS is comprised of a conventional fuzzy logic controller in the feedback configuration and a recurrent neural network in the inverse mode (feedforward) configuration. It is envisaged that the integration of fuzzy logic-and neural networks—based systems will encompass the merits of both the techniques, and thus provide an intelligent and a robust mechanism. The NFS uses minimum fuzzy rules for a given task, thus, overcoming the problem of designing an elaborate Knowledge Base for the Fuzzy Logic Controller. The proposed scheme is implemented for computing inverse kinematic transformations of a two-linked robot. It should be noted, however, that the NFS is not to be considered as a ‘Fuzzy Neural Network’; but it is a synthesis of neural networks and fuzzy logic based control techniques.