Showing papers in "Fuzzy Sets and Systems in 1999"

TL;DR: The theory of possibility described in this paper is related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable.

TL;DR: This paper focuses on semantic approaches to approximate reasoning based on fuzzy sets, commonly exemplified by the generalized modus ponens, but also considers applications to current topics in Artificial Intelligence such as default reasoning and qualitative process modeling.

TL;DR: The interval-valued possibilistic mean value is defined and the variance of linear combination of fuzzy numbers can be computed in a similar manner as in probability theory.

TL;DR: It is shown that the maxima methods behave well with respect to the more basic defuzzification criteria, and hence are good candidates for fuzzy reasoning systems and the distribution methods and the area methods do not fulfill the basic criteria but they exhibit the property of continuity that makes them suitable for fuzzy controllers.

TL;DR: This work and its conclusions may narrow the gap between the theoretical research on FDEs and FIEs and the practical applications already existing in the design of various fuzzy dynamical systems.

TL;DR: A neuro-fuzzy architecture for function approximation based on supervised learning that is an extension to the already published NEFCON and NEFCLASS models and can be used for any application based on function approximation.

TL;DR: A scheme based on the classical Euler method is discussed in detail, and this is followed by a complete error analysis, illustrated by solving several linear and nonlinear fuzzy Cauchy problems.

TL;DR: It is shown that the direct product of t-norms without zero divisors is again a t- norm without zero Divisors and that in the case of the real unit square, transformations by means of an automorphism preserve thedirect product structure.

TL;DR: A new fuzzy arithmetic is defined and applied to fuzzy linear equations and fuzzy calculus based on a new parametric form presented in this paper.

TL;DR: This paper discusses the voting by multiple fuzzy if-then rules, which is used as a fuzzy reasoning method for classifying input patterns in a single fuzzy rule-based classification system, and compares it with other classification methods such as neural networks and statistical techniques by computer simulations on some well-known test problems.

TL;DR: This paper first characterize the feasible domain and then converts the problem to an equivalent problem involving 0–1 integer programming with a branch-and-bound solution technique, and presents the solution procedure.

TL;DR: The method utilizes fuzzy ratio scales 1 , 3 , 5 , 7 , 9 (the goal is normalizing heterogeneity into homogeneity) to indicate the relative strength of the factors in the corresponding criteria to determine the best weapon selection by ranking fuzzy numbers.

TL;DR: Ranking alternatives (both qualitative as well as quantitative) in a multicriterion environment, employing experts opinion (preference structure) using fuzzy numbers and linguistic variables, are presented in this paper.

TL;DR: A method to calculate the correlation coefficient for fuzzy data is proposed, but rather than defining the correlation on the intuitionistic fuzzy sets like most of the previous works, the method is adopted from mathematical statistics.

TL;DR: This work presents an alternative approach to generate fuzzy rules with a functional consequent associated to the TSK fuzzy model using fuzzy clustering algorithms that look for linear behaviours in the product space of the input-output data.

TL;DR: A fuzzy multicriteria evaluation model is developed to study the trend of urban development and results indicate that the proposed model is able to represent those important factors affecting urban development layer-by-layer and stage- by-stage.

TL;DR: A revised fuzzy-set interpretation of possibility theory is introduced in this article, which is shown to be coherent for all fuzzy sets and is also argued that the revised interpretation which coincides with the standard one for normal fuzzy sets is more meaningful on intuitive grounds.

TL;DR: The relations between fuzzy entropy, fuzzy Hamming distance, divergence measuredefined by Bhandari and Pal (1993), and divergence measure defined in this paper are studied and quite a general conclusion is obtained.

TL;DR: This work improves Hong and Lee's general learning method by first selecting relevant attributes and building appropriate initial membership functions, which effectively induces membership functions and fuzzy if-then rules.

TL;DR: It is shown that it is possible to induce equivalence classes from proximity relations; thus, the ‘nice’ properties of the fuzzy relational model of Buckles and Petry are preserved and the removal of the max-min transitivity restriction also provides database users with more freedom to express their value structures.

TL;DR: This paper considers solutions to elementary fuzzy partial differential equations and follows the same strategy as in Buckley and Feuring, which is to first check to see if the Buckley-Feuring method produces a solution; and if it does not give a solution, if the Seikkala procedure generates a solution.

TL;DR: The results of a fuzzy linear regression model that uses symmetric triangular coefficient to one with non-symmetric fuzzy triangular coefficients are extended, eradicating the inflexibility of existing fuzzylinear regression models.

TL;DR: A new family of filters for images corrupted by impulse noise and a new class of noise-protected operators for edge detection that perform significantly better than other techniques in the literature are presented.

TL;DR: A hybrid algorithm for extracting important fuzzy rules from a given rule base to construct a “parsimonious” fuzzy model with a high generalization ability is proposed that combines the advantages of genetic algorithms' strong search capacity and Kalman filter's fast convergence merit.

TL;DR: This work proposes a heuristic method to calibrate the fuzzy exponent iteratively and a hybrid learning algorithm for refining the system parameters, based on the fuzzy c-means (FCM) Bezdek (1987) clustering algorithm.

TL;DR: The proposed methods can overcome the drawbacks in Biswas (1995) due to the fact that they do not need to perform the complicated matching operations and they can evaluate students' answerscripts in a more fair manner.

TL;DR: A version of the classical Peano's theorem for the initial value problem for a fuzzy differential equation in the metric space of normal fuzzy convex sets with distance given by the maximum of the Hausdorff distances between level sets is proved.

TL;DR: A two- phase approach to solve the fuzzy linear programming problem has the merit stated as: if the decision maker is seeking an efficient solution which can improve the max-min operators' solution so that each membership degree should be improved, then the two-phase method automatically attains this desire if there is room to improve.

TL;DR: A specially designed genetic algorithm is employed to realize the optimization in determining the nonadditive set function from given input-output data when the nonlinear integral is viewed as a multi-input single-output system.

TL;DR: A simple but powerful FLC (Fuzzy Logic Controller) design method using a single fuzzy input variable, which is equivalent to the pseudo sliding mode controller, and hence, the stability is guaranteed using the Lyapunov stability.