DOCSLIB.ORG

Systematic Methods for the Design of a Class of Fuzzy Logic Controllers

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Systematic Methods for the Design of a Class of Fuzzy Logic Controllers Western Michigan University ScholarWorks at WMU Dissertations Graduate College 4-2002 Systematic Methods for the Design of a Class of Fuzzy Logic Controllers Saad Y. Yasin Western Michigan University Follow this and additional works at: https://scholarworks.wmich.edu/dissertations Part of the Navigation, Guidance, Control and Dynamics Commons Recommended Citation Yasin, Saad Y., "Systematic Methods for the Design of a Class of Fuzzy Logic Controllers" (2002). Dissertations. 1342. https://scholarworks.wmich.edu/dissertations/1342 This Dissertation-Open Access is brought to you for free and open access by the Graduate College at ScholarWorks at WMU. It has been accepted for inclusion in Dissertations by an authorized administrator of ScholarWorks at WMU. For more information, please contact [email protected]. SYSTEMATIC METHODS FOR THE DESIGN OF A CLASS OF FUZZY LOGIC CONTROLLERS By Saad Y. Yasin A Dissertation Submitted to the Faculty of the Graduate College in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Department of Mechanical and Aeronautical Engineering Western Michigan University Kalamazoo, Michigan April 2002 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. SYSTEMATIC METHODS FOR THE DESIGN OF A CLASS OF FUZZY LOGIC CONTROLLERS Saad Y. Yasin, Ph.D. Western Michigan University, 2002 Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental data, and a conversion algorithm, to develop a fuzzy-based control algorithm. Results were similar to those obtained by recently published conventional control based studies. The influence of the fuzzy inference operators and parameters on performance and stability of the fuzzy logic controller was studied. Results indicated that, the selections of certain parameters or combinations of parameters, affect greatly the performance and stability of the fuzzy controller. Diagnostic guidelines used to tune or change certain factors or parameters to improve controller performance were developed based on knowledge gained from conventional control methods and knowledge gained from the experimental and the simulation results of this study. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI 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 bteedthrough, 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. 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. ProQuest Information and Learning 300 North Zeeb Road, Ann Arbor, Ml 48106-1346 USA 800-521-0600 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. UMI Number 3057849 Copyright 2002 by Yasin, Saad Yaser All rights reserved. UMI* UMI Microform 3057849 Copyright 2002 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest Information and Learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, Ml 48106-1346 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Copyright by Saad Y. Yasin 2002 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ACKNOWLEDGMENTS This work would not have been possible without the help and support of many people. First, I would like to thank Dr. Rameshwar Sharma for his support, confidence, encouragement, advice, and valuable guidance throughout the many years of working together. I would also like to thank Dr. Subramaniam Ganesan of the Department of Computer Science and Engineering at Oakland University for his valuable guidance, stimulating thoughts and discussions, and for allowing me to use the research facilities at Oakland University. I would also like to thank Dr. Gurbux S. Alag of the Department of Electrical Engineering for his valuable and informative discussions, inputs, and feedback that made this work rich in its content and broader in its scope. I would also like to thank all members of my doctoral committee: Dr. Jerry Hamelink and Dr. Koorosh Naghshineh for their advice and guidance during my graduate studies and research. My thanks and appreciation also go to Dr. Maurice Snyder, Dr. Tim Athan, and Dr. Marcella Haghgodie at the Applied Dynamics International for their help in the experimental work conducted at their facilities. I want to express my appreciation and thanks to my wife and family, especially my parents who were very patient, understanding, and supportive. Last but not least I would like to thank Hope E. Smith who through her professionalism in the typing and editing part of this work helped greatly in producing this work in its present form. Saad Y. Yasin ii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. TABLE OF CONTENTS ACKNOWLEDGMENTS.................................................................................. ii LIST OF TABLES.............................................................................................. ix LIST OF FIGURES............................................................................................ x CHAPTER I. RESEARCH OVERVIEW...................................................................... 1 Literature Review.............................................................................. 12 Dissertation Motivation .................................................................... 16 Dissertation Organization ................................................................. 22 II. BASIC MATHEMATICAL CONCEPTS OF FUZZY SETS AND NUMBERS.................................................................................... 25 Introduction ....................................................................................... 25 Fuzzy Sets and Crisp Sets ................................................................. 26 Membership Function ................................................................. 30 Fuzzy Set Operations .................................................................. 33 Linguistic Hedges and Operators ...................................................... 40 Fuzzy Inference Rules ....................................................................... 40 Fuzzy Logic Models .........................................................................
Load more

Recommended publications
  • 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
    [Show full text]
  • The Orthogonality Between Complex Fuzzy Sets and Its Application to Signal Detection
    Article The Orthogonality between Complex Fuzzy Sets and Its Application to Signal Detection Bo Hu 1 ID , Lvqing Bi 2 and Songsong Dai 3,* ID 1 School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China; [email protected] 2 School of Electronics and Communication Engineering, Yulin Normal University, Yulin 537000, China; [email protected] 3 School of Information Science and Engineering, Xiamen University, Xiamen 361005, China * Correspondence: [email protected]; Tel.: +86-592-258-0135 Academic Editor: Hsien-Chung Wu Received: 25 July 2017; Accepted: 25 August 2017; Published: 31 August 2017 Abstract: A complex fuzzy set is a set whose membership values are vectors in the unit circle in the complex plane. This paper establishes the orthogonality relation of complex fuzzy sets. Two complex fuzzy sets are said to be orthogonal if their membership vectors are perpendicular. We present the basic properties of orthogonality of complex fuzzy sets and various results on orthogonality with respect to complex fuzzy complement, complex fuzzy union, complex fuzzy intersection, and complex fuzzy inference methods. Finally, an example application of signal detection demonstrates the utility of the orthogonality of complex fuzzy sets. Keywords: orthogonality; complex fuzzy sets; complex fuzzy operations; complex fuzzy inference 1. Introduction Complex fuzzy sets [1] are an important extension of fuzzy set theory. In recent years, complex fuzzy sets have been successfully used in complex fuzzy inference systems for various applications, such as time series prediction [2–8], function approximation [9–11], and image restoration [12,13]. A complex fuzzy set A on a universe of discourse U is a mapping from U to the unit disc in the complex plane.
    [Show full text]
  • 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
    [Show full text]
  • Fuzzy Relational Maps and Neutrosophic Relational Maps
    University of New Mexico UNM Digital Repository Faculty and Staff Publications Mathematics 2004 FUZZY RELATIONAL MAPS AND NEUTROSOPHIC RELATIONAL MAPS Florentin Smarandache University of New Mexico, [email protected] W.B. Vasantha Kandasamy [email protected] Follow this and additional works at: https://digitalrepository.unm.edu/math_fsp Part of the Algebraic Geometry Commons, Analysis Commons, and the Set Theory Commons Recommended Citation W.B. Vasantha Kandasamy & F. Smarandache. FUZZY RELATIONAL MAPS AND NEUTROSOPHIC RELATIONAL MAPS. Church Rock: Hexis, 2004. 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]. W. B. VASANTHA KANDASAMY FLORENTIN SMARANDACHE FUZZY RELATIONAL MAPS AND NEUTROSOPHIC RELATIONAL MAPS HEXIS Church Rock 2004 FUZZY RELATIONAL MAPS AND NEUTROSOPHIC RELATIONAL MAPS W. B. Vasantha Kandasamy Department of Mathematics Indian Institute of Technology, Madras Chennai – 600036, India e-mail: [email protected] web: http://mat.iitm.ac.in/~wbv Florentin Smarandache Department of Mathematics University of New Mexico Gallup, NM 87301, USA e-mail: [email protected] HEXIS Church Rock 2004 1 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/ and online from: Publishing Online, Co.
    [Show full text]
  • Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic
    NASA Conference Publication 10111 Proceedings of the Third - _ International Workshop on Neural Networks and T_ Fuzzy Logic , k ('_ASA-CP-1OIII-Vol-2) PRQCEEOINGS N93-22206 i]F ThE TH[_O INTERNATIONAL WORKSHOP --THRU-- ON NEURAL NETWORKS AND FUZZY LOGIC, N93-22223 V_LU_E 2 (NASA) i83 p Unclas Volume II G3/63 0150400 a workshop held at ,nson Space Center Houston, Texas June 1 - 3, 1992 p_ _q_r NASA Conference Publication 10111 Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic Volume II Christopher J. Culbert, Editor NASA Lyndon B. Johnson Space Center Houston, Texas Proceedings of a workshop held at Lyndon B. Johnson Space Center Houston, Texas June 1 - 3, 1992 National Aeronautics and Space Administration January 1993 THIRD INTERNATIONAL WORKSHOP ON NEURAL NETWORKS AND FUZZY LOGIC Program Schedule Monday June 1, 1992 7:30-8:00 Registration 8:00-8:30 Robed T. Savely, Chief Scientist, Information Systems Directorate, NASA/Lyndon B. Johnson Space Center, Houston, TX. Welcoming Remarks. 8:30-9:30 Jon Erickson, Chief Scientist, Automation and Robotics Division, NASNLyndon B. Johnson Space Center, Houston, TX. Space Exploration Needs for Supervised Intelligent Systems. 9:30-9:45 Break Plenary Speakers 9:45-10:30 Piero P. Bonnisone, General Electric, Fuzzy Logic Controllers: A Knowledge-Based Systems Perspective. 10:30-11:15 Robed Farber, Los Alamos National Laboratory, Efficiently Modeling Neural Networks on Massively Parallel Computers. 11:15-1:00 Lunch pRLI_EOING P.._SE BLANK NOT RLMIED iii. w 1:00-1:30 Lawrence O. Hall and Steve G. Romaniuk, University of South Florida, Learning Fuzzy Information in a Hybrid Connectionist, Symbolic Model.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • An Introduction to Fuzzy & Annotated Semantic Web Languages Arxiv
    An Introduction to Fuzzy & Annotated Semantic Web Languages∗ Umberto Straccia ISTI - CNR, Pisa, ITALY [email protected] Abstract We present the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions. 1 Introduction Managing uncertainty and fuzziness is growing in importance in Semantic Web re- search as recognised by a large number of research efforts in this direction [279, 285, 287, 288, 291]. Semantic Web Languages (SWL) are the languages used to provide a formal description of concepts, terms, and relationships within a given domain, among which the OWL 2 family of languages [221], triple languages RDF & RDFS [64] and rule languages (such as RuleML [138], Datalog± [68] and RIF [237]) are major players. While their syntactic specification is based on XML [313], their semantics is based arXiv:1811.05724v1 [cs.AI] 14 Nov 2018 on logical formalisms: briefly, • RDFS is a logic having intensional semantics and the logical counterpart is ρdf [219]; ∗This is an updated version of [291] and acts as accompanying material to my invited talk and slides at the 2018 Artificial Intelligence International Conference (A2IC-18). 1 • OWL 2 is a family of languages that relate to Description Logics (DLs) [4]; • rule languages relate roughly to the Logic Programming (LP) paradigm [177]; • both OWL 2 and rule languages have an extensional semantics.
    [Show full text]
  • Fuzzy Inference Systems for Risk Appraisal in Military Operational Planning Curtis B
    Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 3-21-2019 Fuzzy Inference Systems for Risk Appraisal in Military Operational Planning Curtis B. Nelson Follow this and additional works at: https://scholar.afit.edu/etd Part of the Operations and Supply Chain Management Commons Recommended Citation Nelson, Curtis B., "Fuzzy Inference Systems for Risk Appraisal in Military Operational Planning" (2019). Theses and Dissertations. 2311. https://scholar.afit.edu/etd/2311 This Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. FUZZY INFERENCE SYSTEMS FOR RISK APPRAISAL IN MILITARY OPERATIONAL PLANNING THESIS Curtis B. Nelson, Major, USA AFIT-ENS-MS-19-M-141 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A. APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. The views expressed in this thesis are those of the author and do not reflect the official policy or position of the United States Army, United States Air Force, Department of Defense, or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT-ENS-MS-19-M-141 FUZZY INFERENCE SYSTEMS FOR RISK APRAISAL IN MILITARY OPERATIONAL PLANNING THESIS Presented to the Faculty Department of Operational Sciences Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command In Partial Fulfillment of the Requirements for the Degree of Master of Science in Operations Research Curtis B.
    [Show full text]
  • 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.
    [Show full text]
  • A Fuzzy Model for Representing Uncertain, Subjective and Vague Temporal Knowledge in Ontologies
    A Fuzzy Model for Representing Uncertain, Subjective and Vague Temporal Knowledge in Ontologies G´abor Nagyp´aland Boris Motik FZI Research Center for Information Technologies at the University of Karlsruhe? Haid-und-Neu-Str. 10-14 76131 Karlsruhe, Germany fnagypal,[email protected] http://www.fzi.de Abstract. Time modeling is a crucial feature in many application do- mains. However, temporal information often is not crisp, but is uncertain, subjective and vague. This is particularly true when representing histori- cal information, as historical accounts are inherently imprecise. Similarly, we conjecture that in the Semantic Web representing uncertain temporal information will be a common requirement. Hence, existing approaches for temporal modeling based on crisp representation of time cannot be applied to these advanced modeling tasks. To overcome these difficulties, in this paper we present fuzzy interval-based temporal model capable of representing imprecise temporal knowledge. Our approach naturally subsumes existing crisp temporal models, i.e. crisp temporal relation- ships are intuitively represented in our system. Apart from presenting the fuzzy temporal model, we discuss how this model is integrated with the ontology model to allow annotating ontology definitions with time specifications. 1 Introduction Time modeling is a crucial feature in many application domains, such as medicine, history, criminal and financial applications. Its importance is shown by the nu- merous works in the area of temporal databases [1, 2] and temporal reasoning [3]. Our experience from the EU-IST sponsored VICODI project1 supports this claim fully. The main aim of this project is to develop an ontology of European history used for semantical indexing of historical documents.
    [Show full text]
  • The Implementation of Intelligent Oos Networking by the Development
    Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2003 The Implementation of Intelligent QoS Networking by the Development and Utilization of Novel Cross-Disciplinary Soft Computing Theories and Techniques Ahmed Shawky Moussa Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF ARTS AND SCIENCES THE IMPLEMENTATION OF INTELLIGENT QoS NETWORKING BY THE DEVELOPMENT AND UTILIZATION OF NOVEL CROSS-DISCIPLINARY SOFT COMPUTING THEORIES AND TECHNIQUES By AHMED SHAWKY MOUSSA A Dissertation submitted to the Department of Computer Science In partial fulfillment of the requirements for The degree of Doctor of Philosophy Degree Awarded: Fall Semester, 2003 The members of the committee approve the dissertation of Ahmed Shawky Moussa Defended on September 19, 2003. ______________________ Ladislav Kohout Major Professor ______________________ Michael Kasha Outside Committee Member ______________________ Lois Wright Hawkes Committee Member ______________________ Ernest McDuffie Committee Member ______________________ Xin Yuan Committee Member The Office of Graduate Studies has verified and approved the above named committee members ii Optimization is not just a technique, or even a whole science; it is an attitude and a way of life. My mother’s life is a great example of optimization, making the best out of every thing, sometimes out of nothing. She has also been the greatest example of sacrifice, dedication, selfishness, and many other qualities, more than can be listed. I dedicate this work and all my past and future works to the strongest person I have ever known, the one who taught me, by example, what determination is, Fawkeya Ibrahim, my mother.
    [Show full text]