DOCSLIB.ORG

Fuzzy Logic S from Wikipedia, the Free Encyclopedia Contents

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Fuzzy Logic S from Wikipedia, the Free Encyclopedia Contents Fuzzy logic s From Wikipedia, the free encyclopedia Contents 1 Adaptive neuro fuzzy inference system 1 1.1 References ............................................... 1 2 Bate’s chip 2 2.1 References ............................................... 2 3 BL (logic) 3 3.1 Syntax ................................................. 3 3.1.1 Language ........................................... 3 3.1.2 Axioms ............................................ 4 3.2 Semantics ............................................... 4 3.3 Bibliography ............................................. 5 3.4 References .............................................. 5 4 Combs method 6 4.1 Equality proof ............................................. 6 4.2 Combinatorial explosion ....................................... 6 4.3 Example ............................................... 6 4.4 References .............................................. 7 5 Construction of t-norms 8 5.1 Generators of t-norms ........................................ 8 5.1.1 Additive generators ...................................... 8 5.1.2 Multiplicative generators ................................... 9 5.2 Parametric classes of t-norms ..................................... 9 5.2.1 Schweizer–Sklar t-norms ................................... 10 5.2.2 Hamacher t-norms ...................................... 10 5.2.3 Frank t-norms ........................................ 11 5.2.4 Yager t-norms ........................................ 11 5.2.5 Aczél–Alsina t-norms .................................... 11 5.2.6 Dombi t-norms ........................................ 12 5.2.7 Sugeno–Weber t-norms ................................... 12 5.3 Ordinal sums ............................................. 13 5.3.1 Ordinal sums of continuous t-norms ............................. 14 i ii CONTENTS 5.4 Rotations ............................................... 14 5.5 See also ................................................ 15 5.6 References .............................................. 16 6 Defuzzification 17 6.1 Methods ................................................ 17 6.2 Notes ................................................. 18 6.3 See also ................................................ 18 7 Degree of truth 19 7.1 See also ................................................ 19 7.2 Bibliography ............................................. 19 8 European Society for Fuzzy Logic and Technology 20 8.1 History ................................................. 20 8.2 Conferences .............................................. 20 8.3 Publications .............................................. 21 8.4 Presidents ............................................... 21 8.5 References ............................................... 21 8.6 External links ............................................. 21 9 Fuzzy architectural spatial analysis 22 9.1 Overview ............................................... 22 9.2 References ............................................... 22 9.3 Further reading ............................................ 22 9.4 See also ................................................ 23 10 Fuzzy associative matrix 24 11 Fuzzy classification 25 11.1 Classification ............................................. 25 11.2 See also ................................................ 26 11.3 References ............................................... 26 12 Fuzzy cognitive map 27 12.1 Details ................................................. 27 12.2 References .............................................. 29 12.3 External links ............................................. 29 13 Fuzzy Control Language 30 13.1 External links ............................................. 30 14 Fuzzy control system 31 14.1 Overview ............................................... 31 14.2 History and applications ....................................... 31 CONTENTS iii 14.3 Fuzzy sets ............................................... 32 14.3.1 Fuzzy control in detail .................................... 33 14.3.2 Building a fuzzy controller .................................. 37 14.4 Antilock brakes ............................................ 38 14.5 Logical interpretation of fuzzy control ................................ 39 14.6 See also ................................................ 39 14.7 References ............................................... 40 14.8 Further reading ............................................ 40 14.9 External links ............................................. 40 15 Fuzzy electronics 41 15.1 See also ................................................ 41 15.2 Bibliography ............................................. 41 15.3 External links ............................................. 41 16 Fuzzy finite element 42 16.1 See also ................................................ 42 16.2 References ............................................... 42 17 Fuzzy logic 43 17.1 Overview ............................................... 43 17.1.1 Applying truth values ..................................... 43 17.1.2 Linguistic variables ...................................... 44 17.2 Early applications ........................................... 44 17.3 Example ................................................ 44 17.3.1 Hard science with IF-THEN rules .............................. 44 17.3.2 Define with multiply ..................................... 45 17.3.3 Define with sigmoid ..................................... 45 17.4 Logical analysis ............................................ 45 17.4.1 Propositional fuzzy logics .................................. 45 17.4.2 Predicate fuzzy logics .................................... 45 17.4.3 Decidability issues for fuzzy logic ............................... 46 17.5 Fuzzy databases ............................................ 46 17.6 Comparison to probability ....................................... 46 17.7 Relation to ecorithms ......................................... 47 17.8 Compensatory fuzzy logic ...................................... 47 17.9 See also ................................................ 47 17.10References ............................................... 48 17.11Bibliography ............................................. 49 17.12External links ............................................. 51 18 Fuzzy markup language 52 18.1 Overview ............................................... 52 iv CONTENTS 18.2 FML at work: syntax, grammar and hardware synthesis ........................ 52 18.2.1 FML Syntax .......................................... 53 18.2.2 FML Grammar ........................................ 55 18.2.3 FML Synthesis ........................................ 56 18.3 References ............................................... 56 18.4 Further reading ............................................ 57 19 Fuzzy mathematics 58 19.1 Some fields of mathematics using fuzzy set theory .......................... 58 19.2 See also ................................................ 59 19.3 References ............................................... 59 19.4 External links ............................................. 60 20 Fuzzy measure theory 61 20.1 Definitions ............................................... 61 20.2 Properties of fuzzy measures ..................................... 61 20.3 Möbius representation ......................................... 62 20.4 Simplification assumptions for fuzzy measures ............................ 62 20.4.1 Sugeno λ-measure ....................................... 62 20.4.2 k-additive fuzzy measure ................................... 63 20.5 Shapley and interaction indices .................................... 63 20.6 See also ................................................ 63 20.7 References ............................................... 63 20.8 External links ............................................. 63 21 Fuzzy number 64 21.1 See also ................................................ 64 21.2 References ............................................... 64 21.3 External links ............................................. 64 21.4 Applications .............................................. 64 22 Fuzzy pay-off method for real option valuation 65 22.1 Method ................................................ 65 22.2 Use of the method ........................................... 66 22.3 References ............................................... 66 22.4 External links ............................................. 66 23 Fuzzy routing 67 23.1 See also ................................................ 67 23.2 External links ............................................. 67 24 Fuzzy rule 68 24.1 Comparison between Boolean and fuzzy logic rules ......................... 68 24.2 Comparison between computational verb and fuzzy logic rules .................... 68 CONTENTS v 24.3 See also ................................................ 68 25 Fuzzy set 69 25.1 Definition ............................................... 69 25.2 Fuzzy logic .............................................. 69 25.3 Fuzzy number ............................................. 70 25.4 Fuzzy interval ............................................. 70 25.5 Fuzzy relation equation ........................................ 70 25.6 Axiomatic definition of credibility ................................... 70 25.7 Credibility inversion theorem ..................................... 70 25.8 Expected Value ............................................ 71 25.9 Entropy ................................................ 71 25.10Generalizations
Load more

Recommended publications
  • Does Informal Logic Have Anything to Learn from Fuzzy Logic?
    University of Windsor Scholarship at UWindsor OSSA Conference Archive OSSA 3 May 15th, 9:00 AM - May 17th, 5:00 PM Does informal logic have anything to learn from fuzzy logic? John Woods University of British Columbia Follow this and additional works at: https://scholar.uwindsor.ca/ossaarchive Part of the Philosophy Commons Woods, John, "Does informal logic have anything to learn from fuzzy logic?" (1999). OSSA Conference Archive. 62. https://scholar.uwindsor.ca/ossaarchive/OSSA3/papersandcommentaries/62 This Paper is brought to you for free and open access by the Conferences and Conference Proceedings at Scholarship at UWindsor. It has been accepted for inclusion in OSSA Conference Archive by an authorized conference organizer of Scholarship at UWindsor. For more information, please contact [email protected]. Title: Does informal logic have anything to learn from fuzzy logic? Author: John Woods Response to this paper by: Rolf George (c)2000 John Woods 1. Motivation Since antiquity, philosophers have been challenged by the apparent vagueness of everyday thought and experience. How, these philosophers have wanted to know, are the things we say and think compatible with logical laws such as Excluded Middle?1 A kindred attraction has been the question of how truth presents itself — relatively and in degrees? in approximations? in resemblances, or in bits and pieces? F.H. Bradley is a celebrated (or as the case may be, excoriated) champion of the degrees view of truth. There are, one may say, two main views of error, the absolute and relative. According to the former view there are perfect truths, and on the other side there are sheer errors..
    [Show full text]
  • Finitely-Valued Propositional Dynamic Logic
    Finitely-valued Propositional Dynamic Logic Igor Sedl´ar∗ The Czech Academy of Sciences, Institute of Computer Science Pod Vod´arenskou vˇeˇz´ı271/2 Prague, The Czech Republic Abstract We study a many-valued generalization of Propositional Dynamic Logic where formulas in states and accessibility relations between states of a Kripke model are evaluated in a finite FL-algebra. One natural interpretation of this framework is related to reasoning about costs of performing structured actions. We prove that PDL over any finite FL-algebra is decidable. We also establish a general completeness result for a class of PDLs based on commutative integral FL-algebras with canonical constants. Keywords: FL-algebras, Many-valued modal logic, Propositional Dynamic Logic, Residuated lattices, Substructural logics, Weighted structures. 1 Introduction Propositional dynamic logic, PDL, is a well-known modal logic formalizing reasoning about structured actions, e.g. computer programs or actions performed by physical agents, and their correctness properties [10, 17]. PDL is subject to two limiting design features. First, being based on classical propositional logic, it formalizes actions that modify values of Boolean variables. A more general setting, one where variables take values from an ar- bitrary set (integers, characters, trees etc.), is offered by variants of first-order Dynamic Logic, DL [16, 17]; these variants, however, are mostly undecidable. Second, PDL can express the fact that one action is guaranteed to attain a certain goal while another action is not, but it is not able to express that one action is a more efficient way of attaining the goal than another action. In other words, accessibility between states mediated by actions arXiv:2012.12133v1 [cs.LO] 22 Dec 2020 is modelled as a crisp rather than a graded relation; the former approach is a convenient idealization, but the latter one is more realistic and often also practically required.
    [Show full text]
  • The Structure of Residuated Lattices
    The Structure of Residuated Lattices Kevin Blount and Constantine Tsinakis May 23, 2002 Abstract A residuated lattice is an ordered algebraic structure L = hL, ∧, ∨, · , e, \ , / i such that hL, ∧, ∨i is a lattice, hL, ·, ei is a monoid, and \ and / are binary operations for which the equivalences a · b ≤ c ⇐⇒ a ≤ c/b ⇐⇒ b ≤ a\c hold for all a, b, c ∈ L. It is helpful to think of the last two operations as left and right division and thus the equivalences can be seen as “di- viding” on the right by b and “dividing” on the left by a. The class of all residuated lattices is denoted by RL. The study of such objects originated in the context of the theory of ring ideals in the 1930’s. The collection of all two-sided ideals of a ring forms a lattice upon which one can impose a natural monoid structure making this object into a residuated lattice. Such ideas were investi- gated by Morgan Ward and R. P. Dilworth in a series of important papers [15], [16],[45], [46], [47] and [48] and also by Krull in [33]. Since that time, there has been substantial research regarding some specific classes of residuated structures, see for example [1], [9], [26] and [38], but we believe that this is the first time that a general structural the- ory has been established for the class RL as a whole. In particular, we develop the notion of a normal subalgebra and show that RL is an “ideal variety” in the sense that it is an equational class in which con- gruences correspond to “normal” subalgebras in the same way that ring congruences correspond to ring ideals.
    [Show full text]
  • Decision Making: Fuzzy Logic
    First, a bit of history, my 1965 paper on fuzzy sets was motivated by my feeling that the then existing theories provided no means of dealing with a pervasive aspect of reality—unsharpness (fuzziness) of class boundaries. Without such means, realistic models of human-centered and biological systems are hard to construct. My expectation was that fuzzy set theory would be welcomed by the scientific communities in these and related fields. Contrary to my expectation, in these fields, fuzzy set theory was met with skepticism and, in some instances, with hostility. What I did not anticipate was that, for many years after the debut of fuzzy set theory, its Decision Making: main applications would be in the realms of engineering systems and consumer products. Fuzzy Logic … Underlying the concept of a linguistic variable is a fact which is widely unrecognized—a fact which relates to the 2018-03-15 concept of precision. Precision has two distinct meanings—precision in value and precision in meaning. The first meaning is traditional. The second meaning is not. The second meaning is rooted in fuzzy logic. -- Lotfi Zadeh, 2013 Questions (N-2) 1. What are the 2 most “complex” decision making techniques we’ve seen? 2. What are their strengths? Weaknesses? 3. What is the key (insight) to their success? 4. What does Planning have that (forward chaining) RBS do not? 5. When do we need a communication mechanism? 2 Questions (N-1) 1. Cooperative problem solving / distributed expertise is using h___ to d__ problems into smaller parts. 2. R__ experts rarely communicate/collaborate.
    [Show full text]
  • Fuzzy Logic Control for Stand-Alone Photovoltaic Energy Conversion
    Fuzzy Logic Control for Stand-alone Photovoltaic Energy Conversion System, and Innovation in Renewable Energy By Zainab Almukhtar A Thesis Submitted to Saint Mary’s University, Halifax, Nova Scotia in Partial Fulfillment of the Requirements for the Degree of Master of Technology Entrepreneurship and Innovation June 29, 2016, Halifax, Nova Scotia Copyright Zainab Almukhtar, 2016 Approved: Dr. Adel Merabet Supervisor Division of Engineering Approved: Dr. Claudia De Fuentes Supervisory Committee Member Management Department, Sobey School of Business Approved: Dr. Rasheed Beguenane External Examiner ECE, Royal Military College Date: June 29th, 2016 Fuzzy Logic Control for Stand-alone Photovoltaic Energy Conversion System, and Innovation in Renewable Energy. by Zainab Almukhtar Abstract In this dissertation, simulation and hardware emulation was implemented to experiment the operation of a power regulation system for stand-alone PV system with DC loads using Fuzzy Logic Control (FLC). The system encompasses the functions of Maximum Power Point Tracking (MPPT) to bring the power to the maximum value, load power regulation and control of battery operation. An algorithm that tracks the maximum power, and the corresponding fuzzy logic controller were developed. An improved method for the battery operation regulation including a fuzzy logic controller was applied. Load voltage regulation was achieved by a modified cascaded PI controller. The power regulation system managed to stabilize the load power, proved fast MPPT tracking and regulation of the battery operation in the presence of fluctuations and fast input variations. The work included a study of the importance of university-industry collaboration to innovation in renewable energy. The current collaboration channels and the contribution of the current work was analysed through a questionnaire directed to the supervisor.
    [Show full text]
  • 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]
  • Commutative Residuated Lattices
    Commutative Residuated Lattices James B. Hart, Lori Rafter, and Constantine Tsinakis September 21, 2000 Abstract A commutative residuated lattice, is an ordered algebraic struc- ture L = (L, ·, ∧, ∨, →, e), where (L, ·, e) is a commutative monoid, (L, ∧, ∨) is a lattice, and the operation → satisfies the equiva- lences a · b ≤ c ⇐⇒ a ≤ b → c ⇐⇒ b ≤ c → a for a, b, c ∈ L. The class of all commutative residuated lattices, denoted by CRL, is a finitely based variety of algebras. Histor- ically speaking, our study draws primary inspiration from the work of M. Ward and R. P. Dilworth appearing in a series of important papers [9] [10], [19], [20], [21] and [22]. In the en- suing decades special examples of commutative, residuated lat- tices have received considerable attention, but we believe that this is the first time that a comprehensive theory on the struc- ture of residuated lattices has been presented from the viewpoint of universal algebra. In particular, we show that CRL is an ”ideal variety” in the sense that its congruences correspond to order- convex subalgebras. As a consequence of the general theory, we present an equational basis for the subvariety CRLc generated by all commutative, residuated chains. We conclude the paper by proving that the congruence lattice of each member of CRLc is an 2000 Mathematics Subject Classification Primary 06B05; Secondary 06A15, 06B10, 06B20, 06D20, 06F05, 06F07, 06F20 1 algebraic, distributive lattice whose meet-prime elements form a root-system (dual tree). This result, together with the main results in [12] and [18], will be used in a future publication to an- alyze the structure of finite members of CRLc.
    [Show full text]
  • Arxiv:1609.05261V1 [Math.LO]
    BL-RINGS OLIVIER A, HEUBO-KWEGNA, CELESTIN LELE, JEAN B. NGANOU Abstract. The main goal of this article is to introduce BL-rings, i.e., com- mutative rings whose lattices of ideals can be equipped with a structure of BL-algebra. We obtain a description of such rings, and study the connec- tions between the new class and well known classes such as multiplications rings, Baer rings, Dedekind rings. Key words: Multiplication ring, Baer ring, subdirectly irreducible ring, BL- ring. 1. Introduction Given any ring (commutative or not, with or without unity) R generated by idempotents, the semiring of ideals of R under the usual operations form a residuated lattice A(R). In recent articles, several authors have investigated classes of rings for which the residuated lattice A(R) an algebra of a well- known subvariety of residuated lattices. For instance, rings R for which A(R) is an MV-algebra, also calledLukasiewicz rings are investigated in [2], rings R for which A(R) is a G¨odel algebra, also called G¨odel rings are investigated in [3], and very recently rings R for which A(R) is an pseudo MV-algebra, also called GeneralizedLukasiewicz rings are investigated in [11]. In the same spirit, the goal of the present article is we introduce and in- vestigate the class of commutative rings R for which A(R) is a BL-algebra, arXiv:1609.05261v1 [math.LO] 17 Sep 2016 also referred to as BL-rings. Among other things, we show that this class is properly contained in the class of multiplication rings as treated in [9], and contains properly each of the classes of Dedekind domains,Lukas iewicz rings, discrete valuation rings, Noetherian multiplication rings.
    [Show full text]
  • The Use of Fuzzy Logic for Artificial Intelligence in Games
    The use of Fuzzy Logic for Artificial Intelligence in Games Michele Pirovano1;2 1Department of Computer Science, University of Milano, Milano, Italy 2Dipartimento di Ingegneria Elettronica e Informazione, Politecnico di Milano, Italy December 7, 2012 Abstract Game AI, like all other aspects of a videogame, exists to pro- pel the higher goal of a videogame: to be fun and entertain. In Game artificial intelligence (game AI) is the branch of other words, the motto of the game AI society is often thought videogame development that is concerned with empowering to be If the player cannot see it, why do it? [21]. The context games with the illusion of intelligence. Game AI borrows is however not so simple nowadays, as partly due to successful many techniques from the broader field of AI, from simple fi- products that have brought forward advanced AI, partly due to nite state machines to state-of-the-art evolutionary algorithms. the ubiquitous online multi-player options that have changed Among these techniques, fuzzy logic is one of the tools that the habits of players, players are now more demanding in re- must be present in the arsenal of a good videogame AI de- gards to AI and more and more game developers have turned veloper, due to the simplicity of its formulation coupled with their attention from scripted and predictable agents to more its expressive power. In this paper, we introduce the field of human-like agents, capable of learning and thus, in the clas- game AI and review the use of fuzzy logic in games, looking sical AI definition, intelligent.
    [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]
  • Applications of Fuzzy Sets Theory
    Francesco Masulli Sushmita Mitra Gabriella Pasi (Eds.) Applications of Fuzzy Sets Theory 7th International Workshop on Fuzzy Logic and Applications, WILF 2007 Camogli, Italy, July 7-10, 2007 Proceedings 4u Springer Table of Contents Advances in Fuzzy Set Theory From Fuzzy Beliefs to Goals 1 Cilia da Costa Pereira and Andrea G.B. Tettamanzi Information Entropy and Co-entropy of Crisp and Fuzzy Granulations 9 Daniela Bianucci, Gianpiero Cattaneo, and Davide Ciucd Possibilistic Linear Programming in Blending and Transportation Planning Problem 20 Bilge Bilgen Measuring the Interpretive Cost in Fuzzy Logic Computations 28 Pascual Julian, Gines Moreno, and Jaime Penabad A Fixed-Point Theorem for Multi-valued Functions with an Application to Multilattice-Based Logic Programming 37 Jesus Medina, Manuel Ojeda-Aciego, and Jorge Ruiz-Calvino Contextualized Possibilistic Networks with Temporal Framework for Knowledge Base Reliability Improvement 45 Marco Grasso, Michele Lavagna, and Guido Sangiovanni Reconstruction of the Matrix of Causal Dependencies for the Fuzzy Inductive Reasoning Method . 53 Guido Sangiovanni and Michele Lavagna Derivative Information from Fuzzy Models 61 Paulo Salgado and Fernando Gouveia The Genetic Development of Uninorm-Based Neurons 69 Angelo Ciaramella, Witold Pedrycz, and Robertfi Tagliaferri Using Visualization Tools to Guide Consensus in Group Decision Making 77 Sergio Alonso, Enrique Herrera- Viedma, Francisco Javier Cabrerizo, Carlos Porcel, and A.G. Lopez-Herrera Reconstruction Methods for Incomplete Fuzzy Preference
    [Show full text]
  • A Thesis Entitled Kidney Compatibility Score Generation for a Donor – Recipient Pair Using Fuzzy Logic by Sampath Kumar Yella
    A Thesis entitled Kidney Compatibility Score Generation for a Donor – Recipient pair Using Fuzzy Logic by Sampath Kumar Yellanki Submitted to Graduate Faculty as partial fulfillment of the requirements for The Master of Science Degree in Engineering ___ _______________________________________ Dr. Devinder Kaur, Committee Chair __________________________________________ Dr. Michael Rees, M.D., Committee Member __________________________________________ Dr. Ezzatollah Salari, Committee Member __________________________________________ Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo December 2012 Copyright 2012, Sampath Kumar Yellanki This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of Kidney Compatibility Score Generation for a Donor – Recipient pair Using Fuzzy Logic by Sampath Kumar Yellanki Submitted to Graduate School as a partial fulfillment of the requirements for The Master of Science Degree in Engineering The University of Toledo December 2012 This thesis, proposes and implements a Fuzzy Logic based Hierarchical model to address the problem of filtering the donor recipient pairs by predicting a kidney compatibility score. Donor – Recipient pair incompatibility is one of the major problems encountered in Renal Transplantation. Kidney Paired Donation is a barter system where the pairs exchange the donor organs to overcome the disadvantage of incompatibility. Identification of such pairs requires a Kidney Transplant Surgeon to evaluate the compatibility of swapped pairs. Unfortunately, such incompatible pairs run into huge numbers which is a herculean task for a surgeon and can prone to human fatigue. This work presents a Hierarchical System developed based on Fuzzy Logic to determine the quality of a Kidney Transplant based on various input parameters.
    [Show full text]