Showing papers in "Fuzzy Sets and Systems in 1994"

TL;DR: In this paper, a Hausdorff topology on a fuzzy metric space was defined, and Baire's theorem for fuzzy metric spaces was proved for the first time in the fuzzy metric domain.

TL;DR: New techniques for handling multicriteria fuzzy decision-making problems based on vague set theory can provide a useful way to efficiently help the decision-maker to make his decisions.

TL;DR: It is shown that under some additional mild assumptions these triangular fuzzy sets comply with a request for a uniformly excited codebook in the case of the input interfaces and a satisfaction of a zero-error reconstruction criterion being formulated for the output interface.

TL;DR: Different operators are defined over the interval valued intuitionistic fuzzy sets and their basic properties are studied.

TL;DR: A fuzzy neural network will be a layered, feedforward, neural net that has fuzzy signals and/or fuzzy weights.

TL;DR: This paper focused on two kinds of linear programmings with fuzzy numbers, called interval number and fuzzy number linearprogrammings, respectively, and gave the numerical solutions of the illustrative examples.

TL;DR: A new class of fuzzy linear regression models based on Tanaka's approach, here all training data influence the estimated interval, and an adaptation of the fuzzy regression equation to new data becomes possible.

TL;DR: A new and general decision making method for evaluating weapon systems using fuzzy AHP based on entropy weight, which will derive the priority among the alternatives by the entropy weight through the use of interval arithmetic, α-cuts, and index of optimism to estimate the degree of satisfaction of the judgement.

TL;DR: A fuzzy neural architecture based upon the notion of T-norm and T-conorm connectives is developed, which has the potential to capture the benefits of the two fascinating fields, fuzzy logic and neural networks, into a single capsule.

TL;DR: A general class of aggregation operators having the properties of monotonicity, symmetry and an identity element are introduced and it is shown how a general description of the fuzzy system modeling technique can be obtained using these operators.

TL;DR: These models are compared with each other and with a time-invariant Markov model using linguistic labels with probability distributions and compared with three traditional time series models, a first-order autoregressive (AR) model and two second-order auto-regressive (AR(2) models, all of which are time- Invariant.

TL;DR: The new method employed to identify the rule relevant nodes before the rules are extracted makes FuNe I suitable for applications with large number of inputs, and optimization of the knowledge base in possible including the tuning of membership functions.

TL;DR: A new algorithm for evaluating weapon systems by Analytical Hierarchy Process (AHP) based on fuzzy scales, which is a multiple criteria decision making approach in a fuzzy environment is proposed and applied to a weapon system evaluation and selection problem.

TL;DR: A new method to analyze fuzzy system reliability using fuzzy number arithmetic operations, where the reliability of each system component is represented by a triangular fuzzy number.

TL;DR: Two similarity measures are proposed: one for the similarity between fuzzy sets and the other between elements in fuzzy sets.

TL;DR: Every comonotonically additive, positively homogeneous functional of bounded variation can be represented as a Choquet integral with respect to a non-monotonic fuzzy measure of boundedVariation.

TL;DR: A revised method for ranking fuzzy numbers with index of optimism and an algorithm for technology transfer strategy selection based on the concepts of fuzzy set theory and the hierarchical structure analysis are proposed.

TL;DR: In this paper interval-valued fuzzy subgroups of Rosenfeld's nature are defined and how the images or inverse-images of i–v fuzzySubgroups become i-v fuzzy sub groups is studied.

TL;DR: An FPS (Fuzzy Project Scheduling) decision support system applied to software project scheduling to allocate resources among dependent activities taking into account one of two criteria: project completion time and maximum lateness, under uncertain time parameters of activities.

TL;DR: F fuzzy set theory is applied to solve the inadequacy of the conventional probability in accounting and processing of built-in uncertainties in the probabilistic data via non-linear programming techniques where the commercial packages are available to use.

TL;DR: This paper applies a genetic algorithm and neighborhood search algorithms (multi-start descent, taboo search and simulated annealing) to the fuzzy flowshop scheduling problems in order to examine the ability of these algorithms to find near optimal solutions.

TL;DR: It is argued that regular fuzzy neural nets, based on standard fuzzy arithmetic and the extension principle, cannot be universal approximators, but hybrid fuzzy neuralnets, not necessarily based onstandard fuzzy arithmetic, can beuniversal approximator.

TL;DR: A fuzzy version of Kohonen's Self-Organizing Map, which replaces the neurons of the original model by fuzzy rules, which are composed of fuzzy sets, which enables it to model a continuous valued function to an arbitrary accuracy.

TL;DR: In this article, the accessibility relation is defined as a reflexive relation on a non-empty set X, and it is shown that modifiers satisfy the Kuratowski Closure Axioms.

TL;DR: The purpose of this paper is to review and examine some of the approaches to fuzzy linear regression, to discuss their strengths and weaknesses relative to each other, and to suggest possible improvements.

TL;DR: The necessary and sufficient condition obtained in the fuzzy vector topology induced by a fuzzy norm is separated and the consequences of this separation are characterised.

TL;DR: Viewing fuzzy control as an interpolation technique in vague environments enables to validate various concepts for the design and tuning of fuzzy controllers and also suggests new methods based on clear semantics.

TL;DR: This paper introduces a language for the representation and manipulation of temporal entities and relations, which captures some of the terms the authors use in their expressions in the natural language and therefore, it is a flexible and powerful interface for those systems in which the representation of fuzzy temporal information is necessary.

TL;DR: It is argued that this technique can produce good approximate solutions by applying it to solve a fuzzy optimization problem by introducing fuzzy genetic algorithms to (approximately) solve fuzzy optimization problems.