One of the main reasons why classical logic is not always the most adequate tool for describing human knowledge is that in many real-life situations, we have some arguments in favor of a certain statement A and some arguments in favor of its negation ¬A. As a result, we want to consider both A and ¬A to be (to some extent) true. Classical logic does not allow us to do that, while in fuzzy logic...

considering the increasing interest in fuzzy theory and possible applications,the concept of fuzzy metric space concept has been introduced by severalauthors from different perspectives. this paper interprets the theory in termsof metrics evaluated on fuzzy numbers and defines a strong hausdorff topology.we study interrelationships between this theory and other fuzzy theories suchas intuitionis...

In this paper, we introduce a stationary M∞-fuzzy metric on the set C B(X), where M∞-fuzzy metric can be thought of as the degree of nearness between two fuzzy sets with respect to any positive real number and C B(X) is the class of fuzzy sets with nonempty bounded closed α-cut sets. Under the φ-contraction conditions, we give some common fixed point theorems for self-mappings in the space C B(X).

this paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. this architecture of articial neural networks, can get a real input vector and calculates its corresponding fu...

This paper develops several expressions to quantify claim provisions to account in financial statements of a non-life insurance company under the hypothesis of a fuzzy environment. Concretely, by applying the expected value of a fuzzy number and the more general concept of value of a fuzzy number to the ANOVA claim predicting model [2] we estimate claim reserves to account in insurer’s balance ...

In this study we suggest a Bayesian approach to fuzzy clustering analysis – the Bayesian fuzzy regression. Bayesian Posterior Odds analysis is employed to select the correct number of clusters for the fuzzy regression analysis. In this study, we use a natural conjugate prior for the parameters, and we find that the Bayesian Posterior Odds provide a very powerful tool for choosing the number of ...

In crisp production inventory problem, let q, a, b, d, R and r be the quantity produced per cycle, the holding cost, production cost, production quantity per day, the total demand quantity and the demand quantity per day, respectively. We consider three fuzzification methods. Firstly, we fuzzify q, a, b, d, R and r to triangular fuzzy numbers. Secondly, we fuzzify d and r to triangular fuzzy nu...

A new method for finding fuzzy optimal solution, the maximum total completion fuzzy time and fuzzy critical path for the given fully fuzzy critical path (FFCP) problems using crisp linear programming (LP) problem is proposed. In this proposed method, all the parameters are represented by triangular fuzzy number. The fuzzy optimal solution of the FFCP problems obtained by the proposed method, do...

2. Fuzzy stochastic processes First we give some mathematical notations regarding fuzzy numbers. Let (Ω,M, P ) be a probability space, where M is a σ-field and P is a non-atomic probability measure. R denotes the set of all real numbers, and let C(R) be the set of all non-empty bounded closed intervals. A ‘fuzzy number’ is denoted by its membership function ã : R → [0, 1] which is normal, upper...

Most conventional data-mining algorithms identify the relationships among transactions using binary values and find rules at a single concept level. Transactions with quantitative values and items with taxonomic relations are, however, commonly seen in real-world applications. Besides, the taxonomic structures may also be represented in a fuzzy way. This paper thus proposes a fuzzy multiple-lev...