The purpose of this paper is to shed light on the concept of fuzzy logic ,its application in linguistics ,especially in language teaching and the fuzziness of some lexical items in English.
Fuzziness means that the semantic boundaries of some lexical items are indefinite and ideterminate.Fuzzy logic provides a very precise approach for dealing with this indeterminacy and uncertainty which grows (among other reasons) out of human behavior and the effect of society.
The concept of fuzzy logic has emerged in the development of the theory of fuzzy set by Lotfi Zadeh(a professor of computer science at the university of California) in 1965.It can be thought of as the application side of the fuzzy set theory. In linguistics, few scholars have dealt with this phenomenon, among them, Lakoff (1972), Labov (1973), and Aitchison (1994). Consequently, very little literature is available on this concept in linguistics. The researcher has to collect bits of information scattered in various fields of knowledge.
The paper is of three sections: section one surveys briefly some of the semantic terms which are closely related to lexical fuzziness, since it is a problem of meaning by nature, like denotation versus connotation and intension versus extension. Section two is of two main parts. The first explores in detail the emergence of fuzzy logic, shedding light on its development and the criticism to it. The second part is devoted to lexical fuzziness, trying to discover its characteristics, the reasons behind its existence in languages, its relation to other similar concepts like vagueness, its application in teaching a second language and some other related titles.
The aim of this paper is to translate the basic properties of the classical complete normed algebra to the complete fuzzy normed algebra at this end a proof of multiplication fuzzy continuous is given. Also a proof of every fuzzy normed algebra without identity can be embedded into fuzzy normed algebra with identity and is an ideal in is given. Moreover the proof of the resolvent set of a non zero element in complete fuzzy normed space is equal to the set of complex numbers is given. Finally basic properties of the resolvent space of a complete fuzzy normed algebra is given.
In the present paper, discuss the concept of fuzzy topological spectrum of a bounded commutative KU-algebra and study some of the characteristics of this topology. Also, we show that the fuzzy topological spectrum of this structure is compact and T1 -space.
In this research for each positive integer integer and is accompanied by connecting that number with the number of Bashz Attabq result any two functions midwives to derive a positive integer so that there is a point
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We introduce the notion of interval value fuzzy ideal of TM-algebra as a generalization of a fuzzy ideal of TM-algebra and investigate some basic properties. Interval value fuzzy ideals and T-ideals are defined and several examples are presented. The relation between interval value fuzzy ideal and fuzzy T-ideal is studied. Abstract We introduce the notion of interval value fuzzy ideal of TM-algebra as a generalization of a fuzzy ideal of TM-algebra and investigate some basic properties. Interval value fuzzy ideals and T- ideals are defined and several examples are presented. The relation between interval value fuzzy ideal and fuzzy T-ideal is studied.
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In this work we proposed a fuzzy wavenet network (FWN), which learns by common back-propagation algorithm to classify medical images. The library of medical image has been analyzed, first. Second, Two experimental tables’ rules provide an excellent opportunity to test the ability of fuzzy wavenet network due to the high level of information variability often experienced with this type of images.
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... Show MoreThis work addressed the assignment problem (AP) based on fuzzy costs, where the objective, in this study, is to minimize the cost. A triangular, or trapezoidal, fuzzy numbers were assigned for each fuzzy cost. In addition, the assignment models were applied on linguistic variables which were initially converted to quantitative fuzzy data by using the Yager’sorankingi method. The paper results have showed that the quantitative date have a considerable effect when considered in fuzzy-mathematic models.