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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. -
Intelligent Multi-Agent Fuzzy Control System Under
INTELLIGENT MULTI -AGENT FUZZY CONTROL SYSTEM UNDER UNCERTAINTY Ben Khayut 1, Lina Fabri 2 and Maya Abukhana 3 1Department of R&D, IDTS at Intelligence Decisions Technologies Systems, Ashdod, Israel [email protected] 2Department of R&D, IDTS at Intelligence Decisions Technologies Systems, Ashdod, Israel [email protected] 3Department of R&D, IDTS at Intelligence Decisions Technologies Systems, Ashdod, Israel [email protected] ABSTRACT The traditional control systems are a set of hardware and software infrastructure domain and qualified personnel to facilitate the functions of analysis, planning, decision-making, management and coordination of business processes. Human interaction with the components of these systems is done using a specified in advance script dialogue "menu", mainly based on human intellect and unproductive use of navigation. This approach doesn't lead to making qualitative decision and effective control, where the situations and processes cannot be structured in advance. Any dynamic changes in the controlled business process make it necessary to modify the script dialogue. This circumstance leads to a redesign of the components of the entire control system. In the autonomous Fuzzy Control System, where the situations are unknown in advance, fuzzy structured and artificial intelligence is crucial, the redesign described above is impossible. To solve this problem, we propose the data, information and knowledge based technology of creation Situational, Intelligent Multi-agent Control System, which interacts with users -
Zerohack Zer0pwn Youranonnews Yevgeniy Anikin Yes Men
Zerohack Zer0Pwn YourAnonNews Yevgeniy Anikin Yes Men YamaTough Xtreme x-Leader xenu xen0nymous www.oem.com.mx www.nytimes.com/pages/world/asia/index.html www.informador.com.mx www.futuregov.asia www.cronica.com.mx www.asiapacificsecuritymagazine.com Worm Wolfy Withdrawal* WillyFoReal Wikileaks IRC 88.80.16.13/9999 IRC Channel WikiLeaks WiiSpellWhy whitekidney Wells Fargo weed WallRoad w0rmware Vulnerability Vladislav Khorokhorin Visa Inc. Virus Virgin Islands "Viewpointe Archive Services, LLC" Versability Verizon Venezuela Vegas Vatican City USB US Trust US Bankcorp Uruguay Uran0n unusedcrayon United Kingdom UnicormCr3w unfittoprint unelected.org UndisclosedAnon Ukraine UGNazi ua_musti_1905 U.S. Bankcorp TYLER Turkey trosec113 Trojan Horse Trojan Trivette TriCk Tribalzer0 Transnistria transaction Traitor traffic court Tradecraft Trade Secrets "Total System Services, Inc." Topiary Top Secret Tom Stracener TibitXimer Thumb Drive Thomson Reuters TheWikiBoat thepeoplescause the_infecti0n The Unknowns The UnderTaker The Syrian electronic army The Jokerhack Thailand ThaCosmo th3j35t3r testeux1 TEST Telecomix TehWongZ Teddy Bigglesworth TeaMp0isoN TeamHav0k Team Ghost Shell Team Digi7al tdl4 taxes TARP tango down Tampa Tammy Shapiro Taiwan Tabu T0x1c t0wN T.A.R.P. Syrian Electronic Army syndiv Symantec Corporation Switzerland Swingers Club SWIFT Sweden Swan SwaggSec Swagg Security "SunGard Data Systems, Inc." Stuxnet Stringer Streamroller Stole* Sterlok SteelAnne st0rm SQLi Spyware Spying Spydevilz Spy Camera Sposed Spook Spoofing Splendide -
A Fuzzy-Rule Based Ontology for Urban Object Recognition
A Fuzzy-rule Based Ontology for Urban Object Recognition Stella Marc-Zwecker, Khalid Asnoune and Cedric´ Wemmert ICube Laboratory, BFO team, University of Strasbourg, CNRS, Illkirch Cedex, Strasbourg, France Keywords: Ontologies, OWL, SWRL, Fuzzy Logic, Urban Object Recognition, Satellite Images. Abstract: In this paper we outline the principles of a methodology for semi-automatic recognition of urban objects from satellite images. The methodology aims to provide a framework for bridging the semantic gap problem. Its principle consists in linking abstract geographical domain concepts with image segments, by the means of ontologies use. The imprecision of image data and of qualitative rules formulated by experts geographers are handled by fuzzy logic mechanisms. We have defined fuzzy rules, implemented in SWRL (Semantic Web Rule Language), which allow classification of image segments in the ontology. We propose some fuzzy classification strategies, which are compared and evaluated through an experimentation performed on a VHR image of Strasbourg region. 1 INTRODUCTION of cognitive vision. Their approach relies on an on- tology of visual concepts, such as colour and texture, In the domain of knowledge representation for image which can be seen as an intermediate layer between recognition, we outline the principles of a method- domain knowledge and image processing procedures. ology for semi-automatic extraction of urban objects However, this kind of learning system requires that from Very High Resolution (VHR) satellite images. the expert produces examples for each of the concepts This methodology relies on the design and implemen- he is looking for.(Athanasiadis et al., 2007) present a tation of ontologies, which are an effective tool for framework for both image segmentation and object domain’s knowledge formalization and exploitation labeling using an ontology in the domain of multime- (Gruber, 1993) and for the implementation of reason- dia analysis. -
Fuzzy Controllers
Fuzzy Controllers PRELIMS.PM5 1 6/13/97, 10:19 AM To my son Dmitry and other students PRELIMS.PM5 2 6/13/97, 10:19 AM Fuzzy Controllers LEONID REZNIK Victoria University of Technology, Melbourne, Australia PRELIMS.PM5 3 6/13/97, 10:19 AM Newnes An imprint of Butterworth-Heinemann Linacre House, Jordan Hill, Oxford OX2 8DP A division of Reed Educational and Professional Publishing Ltd A member of the Reed Elsevier plc group OXFORD BOSTON JOHANNESBURG MELBOURNE NEW DELHI SINGAPORE First published 1997 © Leonid Reznik 1997 All rights reserved. No part of this publication may be reproduced in any material form (including photocopying or storing in any medium by electronic means and whether or not transiently or incidentally to some other use of this publication) without the written permission of the copyright holder except in accordance with the provisions of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Rd, London, England W1P 9HE. Applications for the copyright holder’s written permission to reproduce any part of this publication should be addressed to the publishers. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN 0 7506 3429 4 Library of Congress Cataloguing in Publication Data A catalogue record for this book is available from the Library of Congress Typeset by The Midlands Book Typesetting Company, Loughborough, Leicestershire, England Printed in Great Britain by Biddles -
Fuzzy Sets, Fuzzy Logic and Their Applications • Michael Gr
Fuzzy Sets, Fuzzy Logic and Their Applications • Michael Gr. Voskoglou • Michael Gr. Fuzzy Sets, Fuzzy Logic and Their Applications Edited by Michael Gr. Voskoglou Printed Edition of the Special Issue Published in Mathematics www.mdpi.com/journal/mathematics Fuzzy Sets, Fuzzy Logic and Their Applications Fuzzy Sets, Fuzzy Logic and Their Applications Special Issue Editor Michael Gr. Voskoglou MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin Special Issue Editor Michael Gr. Voskoglou Graduate Technological Educational Institute of Western Greece Greece Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Mathematics (ISSN 2227-7390) (available at: https://www.mdpi.com/journal/mathematics/special issues/Fuzzy Sets). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year, Article Number, Page Range. ISBN 978-3-03928-520-4 (Pbk) ISBN 978-3-03928-521-1 (PDF) c 2020 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND. Contents About the Special Issue Editor ...................................... vii Preface to ”Fuzzy Sets, Fuzzy Logic and Their Applications” ................... -
Temperature Control Using Fuzzy Logic
International Journal of Instrumentation and Control Systems (IJICS) Vol.4, No.1, January 2014 Temperature Control using Fuzzy Logic P. Singhala1, D. N. Shah2, B. Patel3 1,2,3Department of instrumentation and control, Sarvajanik College of Engineering and Technology Surat, Gujarat, INDIA Abstract The aim of the temperature control is to heat the system up todelimitated temperature, afterwardhold it at that temperature in insured manner. Fuzzy Logic Controller (FLC) is best way in which this type of precision control can be accomplished by controller. During past twenty yearssignificant amount of research using fuzzy logichas done in this field of control of non-linear dynamical system. Here we have developed temperature control system using fuzzy logic. Control theory techniques are the root from which convention controllers are deducted. The desired response of the output can be guaranteed by the feedback controller. Keywords Fuzzy logic, Fuzzy Logic Controller (FLC) and temperature control system. 1. Introduction Low cost temperature control using fuzzy logic system block diagram shown in the fig. in this system set point of the temperature is given by the operator using 4X4 keypad. LM35 temperature sensor sense the current temperature. Analog to digital converter convert analog value into digital value and give to the Fuzzy controller. Fig: 1 Temperature control system Controller calculates error between set point value and current value and consider as Input function of fuzzy logic. By fuzzification process controller calculate it membership. After in rule base and inference system output membership value calculated. Defuzzification process calculates actual value of PWM for heater and fan which is output of the temperature control system. -
Fuzzy Controlled Anti-Lock Braking System
International Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 7, Issue 7, ( Part -10) July 2017, pp.80-83 RESEARCH ARTICLE OPEN ACCESS Fuzzy Controlled Anti-Lock Braking System M M Sahu1, Manoj Kumar Nayak2, Nirmalendu Hota3 1,3Assistant Professor, Department of Mechanical Engineering, Gandhi Institute For Technology (GIFT), Bhubaneswar 2Assistant Professor, Department of Mechanical Engineering, Gandhi Engineering College, Bhubaneswar ABSTRACT:This thesis describes an intelligent approach to control an Antilock Braking System (ABS) employing a fuzzy controller. Stopping a car in a hurry on a slippery road can be very challenging. Anti-lock braking systems (ABS) take a lot of the challenge out of this sometimes nerve-wracking event. In fact, on slippery surfaces, even professional drivers can't stop as quickly without ABS as an average driver can with ABS. Anti-lock Brake improves the controllability of vehicles in compare with brake systems lacking ABS. Fuzzy is a multi-valued logic developed to deal with imprecise or vague data. Classical logic holds that everything can be expressed in binary terms: 0 or 1; in terms of Boolean algebra, everything is in one set or another but not in both. Fuzzy logic allows for partial membership in a set, values between 0 and 1. When the approximate reasoning of fuzzy logic is used with an expert system, logical inferences can be drawn from imprecise relationships. Fuzzy anti-lock Braking systems were developed to reduce skidding and maintain steering control when brakes are used in an emergency situation. Fuzzy controllers are potential candidates for the control of non-linear, time variant and complicated systems. -
Fuzzy Logic in Control Systems: Fuzzy Logic Controller, Part I1
!Err TRANSACTIONS ON SYSTEMS, MAN. ANI) C’YHT.RNT.TI(‘S, VOI.. 20, NO. 2. MAR(.II/APRII. 1000 419 Fuzzy Logic in Control Systems: Fuzzy Logic Controller, Part I1 Abstract -During the past several years, fuzzy control has emerged as I) Basic Properties of a Fuuy Implication Function: one of the most active and fruitful areas for research in the applications The choice of a fuzzy implication function involves a of fuzzy set theory, especially in the realm of industrial processes, which number of criteria, which are discussed in [31, [241, [2l, do not lend themselves to control by conventional methods because of a lack of quantitative data regarding the input-output relations. Fuzzy [711, [181, [521, [191, [116], [SI, [72], and [96]. In particular, control is based on fuzzy logic-a logical system that is much closer in Baldwin and Pilsworth [3] considered the following basic spirit to human thinking and natural language than traditional logical characteristics of a fuzzy implication function: fundamen- systems. The fuzzy logic controller (FLC) based on fuzzy logic provides a tal property, smoothness property, unrestricted inference, means of converting a linguistic control strategy based on expert knowl- symmetry of generalized modus ponens and generalized edge into an automatic control strategy. A survey of the FLC is pre- sented; a general methodology for constructing an FLC and assessing its modus tollens, and a measure of propagation of fuzziness. performance is described; and problems that need further research are All of these properties are justified on purely intuitive pointed out. In particular, the exposition includes a discussion of grounds. -
Fuzzy Expert Control Systems: Knowledge Base Validation - Pedro Albertos and Antonio Sala
CONTROL SYSTEMS, ROBOTICS AND AUTOMATION – Vol. XVII - Fuzzy Expert Control Systems: Knowledge Base Validation - Pedro Albertos and Antonio Sala FUZZY EXPERT CONTROL SYSTEMS: KNOWLEDGE BASE VALIDATION Pedro Albertos and Antonio Sala Universidad Politécnica de Valencia, Spain Keywords: Fuzzy systems, Expert systems, Fuzzy logic, Fuzzy control, Expert control, Learning systems, Knowledge-based systems, Knowledge validation, Rulebase validation. Contents 1. Introduction 2. Integrated Control Systems 2.1. The Knowledge Base 3. Fuzzy Expert Control System Methodology 3.1. Fuzzy Control System 3.2.Expert Control System 3.3. Fuzzy Expert Control System 4. Knowledge in Fuzzy Expert Control Systems 5. Main Design Issues 5.1. General and Knowledge Base Structure Issues 5.2. Rule Design and Inference 5.3. User Interface 5.4. The Role of Formal Methods in Expert Fuzzy Control 6. Objectives of Knowledge Validation for Control 7. Inference 7.1. Rules as Equations or Inequalities 7.2. Validation Definitions 7.3. Inference Error 8. Validation of Fuzzy Expert Controllers 8.1. Validation of Rulebases 8.2. Validation of Interpolative Inference Algorithms 8.3.Rule Extraction 8.4. Validation and Rule Extraction in the Zadeh-Mamdani Approach 9. UncertainUNESCO Models – EOLSS 10. Conclusions and Perspectives Glossary Bibliography SAMPLE CHAPTERS Biographical Sketches Summary The integral control of a complex system requires not only well-designed, specific controllers but a common framework to interrelate the local controllers and to coordinate their implicit knowledge. This knowledge is not only expressed by algorithms but also by rules and facts. The use of possibly large rulebases, in expert systems for control, raises a number of questions about the reliability of the system. -
A Comprehensive Study on Fuzzy Inference System and Its Application in the Field of Engineering Sheena a D, M
International Journal of Engineering Trends and Technology (IJETT) – Volume 54 Number 1 December 2017 A Comprehensive Study on Fuzzy Inference System and its Application in the field of Engineering Sheena A D, M. Ramalingam, B. Anuradha *Research Scholar, Anna University Chennai & Assistant Professor, JSRREC, Chennai **Rtd. Director, Institute of Remote Sensing, Anna University Chennai & Principal, JCE Chennai *** Professor & Head, MIET, Chennai Abstract - This paper presents a study report on of fuzzy control systems for the Sendai railway. Fuzzy Control and Fuzzy Inference System (FIS) When railway lines were opened in 1987, fuzzy highlighting the application for Engineering projects. systems were used to control accelerating, braking, Based on the project focus, FIS model is very much and stopping; Takeshi Yamakawa demonstrated an helpful to identify a project performance evaluation. "inverted pendulum" through a set of simple The reasoning processes of a structure of fuzzy rules, dedicated fuzzy logic chips, in experiment in which a the knowledge base of the system is excellent. By vehicle tries to keep a pole mounted on its top by a applying this, with reasoning it is practical to have a hinge. Yamakawa made the demonstration by quantitative assessment of the progress of projects mounting a wine glass containing water and even a and challenges by visualization. In addition, it is live mouse to the top of the pendulum while the possible to identify the positives and negatives of the system maintained stability in both cases. Likewise, planning and execution process for making decisions Japanese engineers developed a fuzzy systems for where improvement is required. Fuzzy concepts are industrial and consumer applications. -
Iterative Learning Fuzzy Inference System
Iterative Learning Fuzzy Inference System S. Ashraf*, E. Muhammad**, F. Rashid and M. Shahzad, * NUST, Rawalpndi, Pakistan, ** NUST, Rawalpindi, Pakistan, *** PAEC, Pakistan, **** NUST, Pakistan Abstract—This paper presents a learning fuzzy controller were published in another seminal paper [5]. Since then which can adapt with changing performance requirements. fuzzy controllers have found many successful industrial During the past decade we have witnessed a rapid growth in applications, have shown significant improvements in the number and variety of applications of fuzzy logic system performance and have proved to be more robust ranging from consumer electronics and industrial process than conventional controllers [6,7,8]. control to decision support system and financial systems. The challenging tasks associated with fuzzy control The fuzzy controller designer faces the challenge of choosing design has always been, to choose appropriate the appropriate membership functions, minimum rule base membership functions, minimum rule base and the most and the most suitable fuzzifier and defuzzifier. Having made suitable fuzzifier and defuzzifiers. Having made these these choices, the fuzzy controller has to be tuned to deliver choices, the fuzzy controller has to be tuned to deliver the the desired response. Multiple simultaneous adjustments desired response. Multiple simultaneous adjustments (rules, membership functions and gains) make the optimum (rules, membership functions and gains) make the tuning even more difficult. It is now realized that complex optimum tuning even more difficult. Many techniques real world problems require intelligent systems that combine knowledge, techniques and methodologies from have been used to overcome this difficulty including various sources. These intelligent systems are supposed to neural networks [9,10], a phase plane technique for FPI possess humanlike expertise within a specific domain, adapt rule base design [11], and gain phase margin analysis themselves and learn to do better in changing environment.