One of the most efficient techniques for processing interval and fuzzy data is a Monte-Carlo type technique of Cauchy deviates that uses Cauchy distributions. This technique is mathematically valid, but somewhat counterintuitive. In this paper, following the ideas of Paul Werbos, we provide a natural neural network explanation for this technique. Keywords— Cauchy deviate method, fuzzy uncertain...

Disassembly is rapidly growing in importance as manufacturers face increasing pressure to deal with obsolete products in an environmentally responsible and economically sound manner. This paper builds upon our previous work to address uncertainty management in disassembly. By taking advantage of fuzzy logic and the formalism of Petri nets, this paper proposes a Fuzzy Disassembly Petri Net (F-DP...

The weight is one of the most useful tools to measure the attribute importance when individuals make a decision or evaluate the alternatives. Among the methods which measure the weight, fuzzy measures is are subjective scales for the degrees of fuzziness and widely used to determine the degrees of subjective importance of evaluation items in numerous studies for the time being. The purpose of t...

in previous studies we first concentrated on utilizing crisp simulationto produce discrete event fuzzy systems simulations. then we extendedthis research to the simulation of continuous fuzzy systems models. in this paperwe continue our study of continuous fuzzy systems using crisp continuoussimulation. consider a crisp continuous system whose evolution depends ondifferential equations. such a ...

This study proposes a novel approach to fuzzy importance-performance analysis (FIPA) to modify conventional importance-performance analysis (IPA) for determining critical attributes in e-learning system measurements. There is an abundance of literature pertaining to the e-learning framework, but there is a shortage of literature on how to implement properly the framework in uncertainty with var...

Fuzzy measure and fuzzy integral theory is an outgrowth of classical measure theory and has found applications in image processing and information fusion. This paper presents the review of a study on the development of a machinery fault diagnosis application of fuzzy measures and fuzzy integrals. Fuzzy measures and fuzzy integrals take into account the index of importance of criteria and intera...

The objective of this paper is to compare probabilistic models and fuzzy set models for design against uncertainty when there is limited information about the statistics of the uncertainty or modeling error. First, we compare the axioms of probabilistic and fuzzy set methods and the rules governing the arithmetic operations that these methods use. Then, we compare the two methods in designing f...

All realistic Multi-Criteria Decision Making (MCDM) problems face various kinds of uncertainty. Since the evaluations of alternatives with respect to the criteria are uncertain they will be assumed to have stochastic nature. To obtain the uncertain optimism degree of the decision maker fuzzy linguistic quantifiers will be used. Then a new approach for fuzzy-stochastic modeling of MCDM problems ...

As far as medical diagnosis problem is concerned, predicting the actual disease in complex situations has been a concerning matter for the doctors/experts. The divergence measure for intuitionistic fuzzy sets is an effective and potent tool in addressing the medical decision making problems. We define a new divergence measure for intuitionistic fuzzy sets (IFS) and its interesting properties ar...