نتایج جستجو برای: valued fuzzy implications

تعداد نتایج: 483920  

2007
Maciej Wygralak

This paper deals with interval-valued fuzzy sets and I-fuzzy sets, Atanassov's intuitionistic fuzzy sets. They are treated in a systematic way as two, formally equivalent, natural extensions of methods of representing incomplete knowledge about sets. We define and investigate triangular norm-based areas of uncertainty of interval-valued fuzzy sets and I-fuzzy sets, and study some properties of ...

1991
E. Trillas

The aim of this paper is to prove that the following two mathematical concepts are equivalent: 1) Fuzzy Preorders which are compatible with an "AND" operation and 2) Fuzzy Consequence Operators (FCO) [8] verifying a finiteness property with respect to that "AND" operation. The role proposed to fuzzy consequences operators is to be a general notion for obtaining approximate consequences from app...

2013
Anjan Mukherjee Abhijit Saha Ajoy Kanti Das

In this paper the concept of interval valued intuitionistic fuzzy soft set relations (IVIFSS-relations for short) is proposed. Our relations on interval valued intuitionistic fuzzy soft sets is an extension of the relations on intuitionistic fuzzy soft sets, introduced by Mukherjee and Chakraborty in 2009. The basic properties of the IVIFSS-relations are also presented and discussed. It is seen...

2009
Aranzazu Jurio Miguel Pagola Daniel Paternain Carlos Lopez-Molina Pedro Melo-Pinto

In this work we use interval-valued fuzzy sets in the Fuzzy C-Means algorithm for image segmentation. We introduce interval-valued restricted equivalence functions as a way of measuring the equivalence between the intervals associated to different pixels. We propose two construction methods for those new functions. We also prove experimentally that with these new concepts, the algorithm provide...

Abolfazl Kazemi, Elahe Mehrzadegan

Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification t...

Journal: :JSW 2012
Minlun Yan

The fuzzy rough set is a fuzzy generalization of the classical rough set. In the traditional fuzzy rough model, the set to be approximated is a fuzzy set. This paper deals with an incomplete fuzzy information system with interval-valued decision by means of generalizing the rough approximation of a fuzzy set to the rough approximation of an interval-valued fuzzy set. Since all condition attribu...

Journal: :international journal of industrial mathematics 0
s. m. ‎mousavi‎ department of industrial engineering‎, ‎faculty of engineering‎, ‎shahed university‎, ‎tehran‎, ‎iran. b. vahdani faculty of industrial and mechanical engineering‎, ‎qazvin branch‎, ‎islamic azad university‎, ‎qazvin‎, ‎iran. h. gitinavard‎ young researchers and elite club‎, ‎south tehran branch‎, ‎islamic azad university‎, ‎tehran‎, ‎iran. h. hashemi‎ young researchers and elite club‎, ‎south tehran branch‎, ‎islamic azad university‎, ‎tehran‎, ‎iran

‎selecting the most suitable robot among their wide range of specifications and capabilities is an important issue to perform the hazardous and repetitive jobs‎. ‎companies should take into consideration powerful group decision-making (gdm) methods to evaluate the candidates or potential robots versus the selected attributes (criteria)‎. ‎in this study‎, ‎a new gdm method is proposed by utilizi...

Journal: :International Journal of Fuzzy Logic and Intelligent Systems 2009

2013
Hong Wang Hong-Bo Yue Xi-E Chen

In many practical situation, some of the attribute values for an object may be interval and set-valued. This paper introduces the interval and set-valued information systems and decision systems. According to the semantic relation of attribute values, interval and set-valued information systems can be classified into two categories: disjunctive (Type 1) and conjunctive (Type 2) systems. In this...

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