Relevancy of Fuzzy Concept in Mathematics
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Critical Review, Vol. X
29 Neutrosophic Vague Set Theory Shawkat Alkhazaleh1 1 Department of Mathematics, Faculty of Science and Art Shaqra University, Saudi Arabia [email protected] Abstract In 1993, Gau and Buehrer proposed the theory of vague sets as an extension of fuzzy set theory. Vague sets are regarded as a special case of context-dependent fuzzy sets. In 1995, Smarandache talked for the first time about neutrosophy, and he defined the neutrosophic set theory as a new mathematical tool for handling problems involving imprecise, indeterminacy, and inconsistent data. In this paper, we define the concept of a neutrosophic vague set as a combination of neutrosophic set and vague set. We also define and study the operations and properties of neutrosophic vague set and give some examples. Keywords Vague set, Neutrosophy, Neutrosophic set, Neutrosophic vague set. Acknowledgement We would like to acknowledge the financial support received from Shaqra University. With our sincere thanks and appreciation to Professor Smarandache for his support and his comments. 1 Introduction Many scientists wish to find appropriate solutions to some mathematical problems that cannot be solved by traditional methods. These problems lie in the fact that traditional methods cannot solve the problems of uncertainty in economy, engineering, medicine, problems of decision-making, and others. There have been a great amount of research and applications in the literature concerning some special tools like probability theory, fuzzy set theory [13], rough set theory [19], vague set theory [18], intuitionistic fuzzy set theory [10, 12] and interval mathematics [11, 14]. Critical Review. Volume X, 2015 30 Shawkat Alkhazaleh Neutrosophic Vague Set Theory Since Zadeh published his classical paper almost fifty years ago, fuzzy set theory has received more and more attention from researchers in a wide range of scientific areas, especially in the past few years. -
The State of the Art in Developing Fuzzy Ontologies: a Survey
The State of the Art in Developing Fuzzy Ontologies: A Survey Zahra Riahi Samani1, Mehrnoush Shamsfard2, 1Faculty of Computer Science and Engineering, Shahid Beheshti University Email:[email protected] 1Faculty of Computer Science and Engineering, Shahid Beheshti University Email:[email protected] Abstract Conceptual formalism supported by typical ontologies may not be sufficient to represent uncertainty information which is caused due to the lack of clear cut boundaries between concepts of a domain. Fuzzy ontologies are proposed to offer a way to deal with this uncertainty. This paper describes the state of the art in developing fuzzy ontologies. The survey is produced by studying about 35 works on developing fuzzy ontologies from a batch of 100 articles in the field of fuzzy ontologies. 1. Introduction Ontology is an explicit, formal specification of a shared conceptualization in a human understandable, machine- readable format. Ontologies are the knowledge backbone for many intelligent and knowledge based systems [1, 2]. However, in some domains, real world knowledge is imprecise or vague. For example in a search engine one may be interested in ”an extremely speedy, small size, not expensive car”. Classical ontologies based on crisp logic are not capable of handling this kind of knowledge. Fuzzy ontologies were proposed as a combination of fuzzy theory and ontologies to tackle such problems. On the topic of fuzzy ontology, we studied about 100 research articles which can be categorized into four main categories. The first category includes the research works on applying fuzzy ontologies in a specific domain- application to improve the performance of the application such as group decision making systems [3] or visual video content analysis and indexing [4].The ontology development parts of the works in this category were done manually or were not of much concentration. -
A Fuzzy Description Logic
From: AAAI-98 Proceedings. Copyright © 1998, AAAI (www.aaai.org). All rights reserved. A Fuzzy Description Logic Umberto Straccia I.E.I - C.N.R. Via S. Maria, 46 I-56126 Pisa (PI) ITALY [email protected] Abstract is such a concept: we may say that an individual tom is an instance of the concept Tall only to a certain degree Description Logics (DLs, for short) allow reasoning n ∈ [0, 1] depending on tom’s height. about individuals and concepts, i.e. set of individuals with common properties. Typically, DLs are limited to Fuzzy logic directly deals with the notion of vague- dealing with crisp, well dened concepts. That is, con- ness and imprecision using fuzzy predicates. Therefore, cepts for which the problem whether an individual is it oers an appealing foundation for a generalisation of an instance of it is a yes/no question. More often than DLs in order to dealing with such vague concepts. not, the concepts encountered in the real world do not The aim of this work is to present a general fuzzy DL, have a precisely dened criteria of membership: we which combines fuzzy logic with DLs. In particular we may say that an individual is an instance of a concept will extend DLs by allowing expressions of the form only to a certain degree, depending on the individual’s hC(a) ni (n ∈ [0, 1]), e.g. hTall(tom) .7i, with intended properties. Concepts of this kind are rather vague than precise. As fuzzy logic directly deals with the notion of meaning “the membership degree of individual a being vagueness and imprecision, it oers an appealing foun- an instance of concept C is at least n”. -
A Unifying Field in Logics: Neutrosophic Logic
University of New Mexico UNM Digital Repository Faculty and Staff Publications Mathematics 2007 A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS - 6th ed. Florentin Smarandache University of New Mexico, [email protected] Follow this and additional works at: https://digitalrepository.unm.edu/math_fsp Part of the Algebra Commons, Logic and Foundations Commons, Other Applied Mathematics Commons, and the Set Theory Commons Recommended Citation Florentin Smarandache. A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS - 6th ed. USA: InfoLearnQuest, 2007 This Book is brought to you for free and open access by the Mathematics at UNM Digital Repository. It has been accepted for inclusion in Faculty and Staff Publications by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected], [email protected], [email protected]. FLORENTIN SMARANDACHE A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS (sixth edition) InfoLearnQuest 2007 FLORENTIN SMARANDACHE A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS (sixth edition) This book can be ordered in a paper bound reprint from: Books on Demand ProQuest Information & Learning (University of Microfilm International) 300 N. Zeeb Road P.O. Box 1346, Ann Arbor MI 48106-1346, USA Tel.: 1-800-521-0600 (Customer Service) http://wwwlib.umi.com/bod/basic Copyright 2007 by InfoLearnQuest. Plenty of books can be downloaded from the following E-Library of Science: http://www.gallup.unm.edu/~smarandache/eBooks-otherformats.htm Peer Reviewers: Prof. M. Bencze, College of Brasov, Romania. -
A Unifying Field in Logics: Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability and Statistics
FLORENTIN SMARANDACHE A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS (fourth edition) NL(A1 A2) = ( T1 ({1}T2) T2 ({1}T1) T1T2 ({1}T1) ({1}T2), I1 ({1}I2) I2 ({1}I1) I1 I2 ({1}I1) ({1} I2), F1 ({1}F2) F2 ({1} F1) F1 F2 ({1}F1) ({1}F2) ). NL(A1 A2) = ( {1}T1T1T2, {1}I1I1I2, {1}F1F1F2 ). NL(A1 A2) = ( ({1}T1T1T2) ({1}T2T1T2), ({1} I1 I1 I2) ({1}I2 I1 I2), ({1}F1F1 F2) ({1}F2F1 F2) ). ISBN 978-1-59973-080-6 American Research Press Rehoboth 1998, 2000, 2003, 2005 FLORENTIN SMARANDACHE A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS (fourth edition) NL(A1 A2) = ( T1 ({1}T2) T2 ({1}T1) T1T2 ({1}T1) ({1}T2), I1 ({1}I2) I2 ({1}I1) I1 I2 ({1}I1) ({1} I2), F1 ({1}F2) F2 ({1} F1) F1 F2 ({1}F1) ({1}F2) ). NL(A1 A2) = ( {1}T1T1T2, {1}I1I1I2, {1}F1F1F2 ). NL(A1 A2) = ( ({1}T1T1T2) ({1}T2T1T2), ({1} I1 I1 I2) ({1}I2 I1 I2), ({1}F1F1 F2) ({1}F2F1 F2) ). ISBN 978-1-59973-080-6 American Research Press Rehoboth 1998, 2000, 2003, 2005 1 Contents: Preface by C. Le: 3 0. Introduction: 9 1. Neutrosophy - a new branch of philosophy: 15 2. Neutrosophic Logic - a unifying field in logics: 90 3. Neutrosophic Set - a unifying field in sets: 125 4. Neutrosophic Probability - a generalization of classical and imprecise probabilities - and Neutrosophic Statistics: 129 5. Addenda: Definitions derived from Neutrosophics: 133 2 Preface to Neutrosophy and Neutrosophic Logic by C. -
Information to Users
INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMl films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6” x 9” black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. UMI A Bell & Howell Infonnation Company 300 North Zeeb Road, Ann Arbor MI 48106-1346 USA 313/761-4700 800/521-0600 AN ANTHROPOLOGICAL INVESTIGATION OF THREE INFORMATION TECHNOLOGY FIRMS USING JAMES C. SCOTT'S THEORIES RELATED TO EVERYDAY FORMS OF RESISTANCE TO POWER DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Cynthia Joseph Smith, B.A., M.A. -
Integrating Fuzzy Logic in Ontologies
INTEGRATING FUZZY LOGIC IN ONTOLOGIES Silvia Calegari and Davide Ciucci Dipartimento di Informatica, Sistemistica e Comunicazione, Universita` degli Studi di Milano Bicocca, via Bicocca degli Arcimboldi 8, 20126 Milano, Italy Keywords: Concept modifiers, fuzzy logics, fuzzy ontologies, membership modifiers, KAON, ontology editor. Abstract: Ontologies have proved to be very useful in sharing concepts across applications in an unambiguous way. Nowadays, in ontology-based applications information is often vague and imprecise. This is a well-known problem especially for semantics-based applications, such as e-commerce, knowledge management, web por- tals, etc. In computer-aided reasoning, the predominant paradigm to manage vague knowledge is fuzzy set theory. This paper presents an enrichment of classical computational ontologies with fuzzy logic to create fuzzy ontologies. So, it is a step towards facing the nuances of natural languages with ontologies. Our pro- posal is developed in the KAON ontology editor, that allows to handle ontology concepts in an high-level environment. 1 INTRODUCTION A possible solution to handle uncertain data and, hence, to tackle these problems, is to incorporate fuzzy logic into ontologies. The aim of fuzzy set An ontology is a formal conceptualization of a partic- theory (Klir and Yuan, 1995) introduced by L. A. ular domain of interest shared among heterogeneous Zadeh (Zadeh, 1965) is to describe vague concepts applications. It consists of entities, attributes, rela- through a generalized notion of set, according to tionships and axioms to provide a common under- which an object may belong to a certain degree (typ- standing of the real world (Lammari and Mtais, 2004; ically a real number from the interval [0,1]) to a set. -
Cotangent Similarity Measures of Vague Multi Sets
International Journal of Pure and Applied Mathematics Volume 120 No. 7 2018, 155-163 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue http://www.acadpubl.eu/hub/ Cotangent Similarity Measures of Vague Multi Sets S. Cicily Flora1 and I. Arockiarani2 1,2Department of Mathematics Nirmala College for Women, Coimbatore-18 Email: [email protected] Abstract This paper throws light on a simplified technique to solve the selection procedure of candidates using vague multi set. The new approach is the enhanced form of cotangent sim- ilarity measures on vague multi sets which is a model of multi criteria group decision making. AMS Subject Classification: 3B52, 90B50, 94D05. Key Words and Phrases: Vague Multi sets, Cotan- gent similarity measures, Decision making. 1 Introduction The theory of sets, one of the most powerful tools in modern mathe- matics is usually considered to have begun with Georg Cantor[1845- 1918]. Considering the uncertainity factor, Zadeh [7] introduced Fuzzy sets in 1965. If repeated occurrences of any object are al- lowed in a set, then the mathematical structure is called multi sets [1]. As a generalizations of multi set,Yager [6] introduced fuzzy multi set. An element of a fuzzy multi set can occur more than once with possibly the same or different membership values. The concept of Intuitionistic fuzzy multi set was introduced by [3]. Sim- ilarity measure is a very important tool to determine the degree of similarity between two objects. Using the Cotangent function, a new similarity measure was proposed by wang et al [5]. -
Fuzzy Types: a Framework for Handling Uncertainty About Types of Objects Tru H
CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector International Journal of Approximate Reasoning 25 (2000) 217±253 www.elsevier.com/locate/ijar Fuzzy types: a framework for handling uncertainty about types of objects Tru H. Cao a,*, Peter N. Creasy b a Department of Engineering Mathematics, Arti®cial Intelligence Group, University of Bristol, Bristol BS8 1TR, UK b Department of Computer Science and Electrical Engineering, University of Queensland, Qld 4072, Australia Received 1 November 1999; received in revised form 1 June 2000; accepted 1 July 2000 Abstract Like other kinds of information, types of objects in the real world are often found to be ®lled with uncertainty and/or partial truth. It may be due to either the vague nature of a type itself or to incomplete information in the process determining it even if the type is crisp, i.e., clearly de®ned. This paper proposes a framework to deal with uncertainty and/or partial truth in automated reasoning systems with taxonomic information, and in particular type hierarchies. A fuzzy type is formulated as a pair combining a basic type and a fuzzy truth-value, where a basic type can be crisp or vague (in the intuitive sense). A structure for a class of fuzzy truth-value lattices is proposed for this con- struction. The fuzzy subtype relation satisfying intuition is de®ned as a partial order between two fuzzy types. As an object may belong to more than one (fuzzy) type, conjunctive fuzzy types are introduced and their lattice properties are studied. -
PREPRINT Intelligence, Science and the Ignorance Hypothesis David R
PREPRINT Intelligence, Science and the Ignorance Hypothesis David R. Mandel Defence Research and Development Canada Intelligence organizations perform many functions. For example, they conduct covert operations that would be too politically sensitive for militaries to undertake as explicit missions. They also collect vast amounts of data that are inaccessible to others, except perhaps other intelligence organizations. However, the principal purpose of information collection is to produce substantive intelligence that can inform policymakers, commanders and other decision-makers who guard their nation’s interests and security. The fundamental premise of intelligence is that it serves to improve the planning and decision-making of these elite decision-makers by elevating the debate about policy options (Kent, 1955). If this were not so, the multi-billion dollar annual budgets would not be justified. If the premise is justified, then clearly the intellectual rigor and accuracy with which intelligence assessments are produce is of paramount importance. How do intelligence organizations ensure that the intelligence assessments they have produced are as accurate and sound as they can be? In this chapter, I will propose that the short answer is “not very well at all.” Methods and policies for ensuring analytic rigor have surely been implemented over the past several decades, but what has been endemic to these efforts is a rather pre-scientific, if not a fully anti-scientific, attitude towards their development and testing. After reviewing some examples of intelligence practices intended to ensure analytic rigor, I advance what I call the ignorance hypothesis to explain the absence of scientific attempts to discern what practices work from those that do not work so that intelligence organizations can effectively learn and adapt to the challenges of the modern world. -
Research Article Vague Filters of Residuated Lattices
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Crossref Hindawi Publishing Corporation Journal of Discrete Mathematics Volume 2014, Article ID 120342, 9 pages http://dx.doi.org/10.1155/2014/120342 Research Article Vague Filters of Residuated Lattices Shokoofeh Ghorbani Department of Mathematics, 22 Bahman Boulevard, Kerman 76169-133, Iran Correspondence should be addressed to Shokoofeh Ghorbani; [email protected] Received 2 May 2014; Revised 7 August 2014; Accepted 13 August 2014; Published 10 September 2014 AcademicEditor:HongJ.Lai Copyright © 2014 Shokoofeh Ghorbani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notions of vague filters, subpositive implicative vague filters, and Boolean vague filters of a residuated lattice are introduced and some related properties are investigated. The characterizations of (subpositive implicative, Boolean) vague filters is obtained. We prove that the set of all vague filters of a residuated lattice forms a complete lattice and we find its distributive sublattices. The relation among subpositive implicative vague filters and Boolean vague filters are obtained and it is proved that subpositive implicative vague filters are equivalent to Boolean vague filters. 1. Introduction of vague ideal in pseudo MV-algebras and Broumand Saeid [4] introduced the notion of vague BCK/BCI-algebras. In the classical set, there are only two possibilities for any The concept of residuated lattices was introduced by Ward elements: in or not in the set. Hence the values of elements and Dilworth [5] as a generalization of the structure of the in a set are only one of 0 and 1. -
A Vague Relational Model and Algebra 1
http://www.paper.edu.cn A Vague Relational Model and Algebra 1 Faxin Zhao, Z.M. Ma*, and Li Yan School of Information Science and Engineering, Northeastern University Shenyang, P.R. China 110004 [email protected] Abstract Imprecision and uncertainty in data values are pervasive in real-world environments and have received much attention in the literature. Several methods have been proposed for incorporating uncertain data into relational databases. However, the current approaches have many shortcomings and have not established an acceptable extension of the relational model. In this paper, we propose a consistent extension of the relational model to represent and deal with fuzzy information by means of vague sets. We present a revised relational structure and extend the relational algebra. The extended algebra is shown to be closed and reducible to the fuzzy relational algebra and further to the conventional relational algebra. Keywords: Vague set, Fuzzy set, Relational data model, Algebra 1 Introduction Over the last 30 years, relational databases have gained widespread popularity and acceptance in information systems. Unfortunately, the relational model does not have a comprehensive way to handle imprecise and uncertain data. Such data, however, exist everywhere in the real world. Consequently, the relational model cannot represent the inherently imprecise and uncertain nature of the data. The need for an extension of the relational model so that imprecise and uncertain data can be supported has been identified in the literature. Fuzzy set theory has been introduced by Zadeh [1] to handle vaguely specified data values by generalizing the notion of membership in a set. And fuzzy information has been extensively investigated in the context of the relational model.