We start by discussing fuzzy sets and the algebra of fuzzy sets. We consider some properties of fuzzy modeling tools. This is followed by considering the Mamdani and Sugeno models for designing fuzzy controllers. Various methods for using sets of data for desining controllers are discussed. This is followed by a chapter illustrating the use of genetic algorithms in designing and optimizing fuzzy controllers.Finally we look at some previous applications of fuzzy control in telecommunication networks, and illustrate a simple application that was developed as part of the present work.

Fuzzy control is a rule based type of control that aims to imitate the human's ability to express a control policy using linguistic rules, and to reason using those rules to control a system. Fuzzy control is nonlinear and not dependent on a precise mathematical description of the plant, and is therefore more easily applied to systems such as industrial processes that are hard to model. An overview is given of the fuzzy controller, along with descriptions of applications and theoretical approaches to designing and analyzing the controller. The self-organizing controller is able to generate or modify its rules in real time based on the system performance. It was tested to determine how well it was able to learn a quality control policy. The self-organizing controller was found to exhibit poor steady state performance, and to be equally likely to learn poor control as to learn good control. It was not found to eliminate the need for careful tuning of the controller parameters and gains. / Master of Science

Various established and emerging applications of RFID technology have been and are being implemented by companies in different parts of the world. However, RFID technology is susceptible to a variety of security and privacy concerns, as it is prone to attacks such as eavesdropping, denial of service, tag cloning and user tracking. This is mainly because RFID tags, specifically low-cost tags, have low computational capability to support complex cryptographic algorithms. Tag cloning is a key problem to be considered since it leads to severe economic losses. One of the possible approaches to address tag cloning is using an intrusion detection system. Intrusion detection systems in RFID networks, on top of the existing lightweight cryptographic algorithms, provide an additional layer of protection where other security mechanisms may fail. This thesis presents an intrusion detection mechanism that detects anomalies caused by one or more cloned RFID tags in the system. We make use of a Hybrid Fuzzy Genetics-Based Machine Learning algorithm to design an intrusion detection model from RFID system-generated event logs. For the purpose of training and evaluation of our proposed approach, part of the RFID system-generated dataset provided by the University of Tasmania’s School of Computing and Information Systems was used, in addition to simulated datasets. The results of our experiments show that the model can achieve high detection rates and low false positive rates when identifying anomalies caused by one or more cloned tags. In addition, the model yields linguistically interpretable rules that can be used to support decision making during the detection of anomaly caused by the cloned tags.

No description available.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy

by Chang Yu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 111-115). / Acknowledgement --- p.i / Abstract --- p.ii / List of Tables --- p.vii / List of Figures --- p.viii / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Outline of the Work of This Thesis --- p.4 / Chapter 1.3 --- Organization of the Thesis --- p.5 / Chapter 2 --- REVIEW OF RELATED WORKS --- p.6 / Chapter 2.1 --- Deduce2 --- p.6 / Chapter 2.2 --- ARES --- p.8 / Chapter 2.3 --- VAGUE --- p.10 / Chapter 2.4 --- Fuzzy Sets-Based Approaches --- p.12 / Chapter 2.5 --- Some General Remarks --- p.14 / Chapter 3 --- A FUZZY DATABASE QUERY LANGUAGE --- p.18 / Chapter 3.1 --- Basic Concepts of Fuzzy Sets --- p.18 / Chapter 3.2 --- The Syntax of the Fuzzy Query Language --- p.21 / Chapter 3.3 --- Fuzzy Operators --- p.25 / Chapter 3.3.1 --- AND --- p.27 / Chapter 3.3.2 --- OR --- p.27 / Chapter 3.3.3 --- COMB --- p.28 / Chapter 3.3.4 --- POLL --- p.28 / Chapter 3.3.5 --- HURWICZ --- p.30 / Chapter 3.3.6 --- REGRET --- p.31 / Chapter 4 --- SYSTEM DESIGN --- p.35 / Chapter 4.1 --- General Requirements and Definitions --- p.35 / Chapter 4.1.1 --- Requirements of the system --- p.36 / Chapter 4.1.2 --- Representation of membership functions --- p.38 / Chapter 4.2 --- Overall Architecture --- p.41 / Chapter 4.3 --- Interface --- p.44 / Chapter 4.4 --- Knowledge Base --- p.46 / Chapter 4.5 --- Parser --- p.51 / Chapter 4.6 --- ORACLE --- p.52 / Chapter 4.7 --- Data Manager --- p.53 / Chapter 4.8 --- Fuzzy Processor --- p.57 / Chapter 5 --- IMPLEMENTION --- p.59 / Chapter 5.1 --- Some General Considerations --- p.59 / Chapter 5.2 --- Knowledge Base --- p.60 / Chapter 5.2.1 --- Converting a concept into conditions --- p.60 / Chapter 5.2.2 --- Concept trees --- p.62 / Chapter 5.3 --- Data Manager --- p.64 / Chapter 5.3.1 --- Some issues on the implementation --- p.64 / Chapter 5.3.2 --- Dynamic library --- p.67 / Chapter 5.3.3 --- Precompiling process --- p.68 / Chapter 5.3.4 --- Calling standard --- p.71 / Chapter 6 --- CASE STUDIES --- p.76 / Chapter 6.1 --- A Database for Job Application/Recruitment --- p.77 / Chapter 6.2 --- Introduction to the Knowledge Base --- p.79 / Chapter 6.3 --- Cases --- p.79 / Chapter 6.3.1 --- Crispy queries --- p.79 / Chapter 6.3.2 --- Fuzzy queries --- p.82 / Chapter 6.3.3 --- Concept queries --- p.85 / Chapter 6.3.4 --- Fuzzy Match --- p.87 / Chapter 6.3.5 --- Fuzzy operator --- p.88 / Chapter 7 --- CONCLUSION --- p.93 / Appendix A Sample Data in DATABASE --- p.96 / Bibliography --- p.111

by John Tak Kuen Koo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 217-[220]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- "Robust, Adaptive and Fuzzy Control" --- p.2 / Chapter 1.3 --- Adaptive Fuzzy Control --- p.4 / Chapter 1.4 --- Object of Study --- p.10 / Chapter 1.5 --- Scope of the Thesis --- p.13 / Chapter 2 --- Background on Adaptive Control and Fuzzy Logic Control --- p.17 / Chapter 2.1 --- Adaptive control --- p.17 / Chapter 2.1.1 --- Model reference adaptive systems --- p.20 / Chapter 2.1.2 --- MIT Rule --- p.23 / Chapter 2.1.3 --- Model Reference Adaptive Control (MRAC) --- p.24 / Chapter 2.2 --- Fuzzy Logic Control --- p.33 / Chapter 2.2.1 --- Fuzzy sets and logic --- p.33 / Chapter 2.2.2 --- Fuzzy Relation --- p.40 / Chapter 2.2.3 --- Inference Mechanisms --- p.43 / Chapter 2.2.4 --- Defuzzification --- p.49 / Chapter 3 --- Explicit Form of a Class of Fuzzy Logic Controllers --- p.51 / Chapter 3.1 --- Introduction --- p.51 / Chapter 3.2 --- Construction of a class of fuzzy controller --- p.53 / Chapter 3.3 --- Explicit form of the fuzzy controller --- p.57 / Chapter 3.4 --- Design criteria on the fuzzy controller --- p.65 / Chapter 3.5 --- B-Spline fuzzy controller --- p.68 / Chapter 4 --- Model Reference Adaptive Fuzzy Control (MRAFC) --- p.73 / Chapter 4.1 --- Introduction --- p.73 / Chapter 4.2 --- "Fuzzy Controller, Plant and Reference Model" --- p.75 / Chapter 4.3 --- Derivation of the MRAFC adaptive laws --- p.79 / Chapter 4.4 --- "Extension to the Multi-Input, Multi-Output Case" --- p.84 / Chapter 4.5 --- Simulation --- p.90 / Chapter 5 --- MRAFC on a Class of Nonlinear Systems: Type I --- p.97 / Chapter 5.1 --- Introduction --- p.98 / Chapter 5.2 --- Choice of Controller --- p.99 / Chapter 5.3 --- Derivation of the MRAFC adaptive laws --- p.102 / Chapter 5.4 --- Example: Stabilization of a pendulum --- p.109 / Chapter 6 --- MRAFC on a Class of Nonlinear Systems: Type II --- p.112 / Chapter 6.1 --- Introduction --- p.113 / Chapter 6.2 --- Fuzzy System as Function Approximator --- p.114 / Chapter 6.3 --- Construction of MRAFC for the nonlinear systems --- p.118 / Chapter 6.4 --- Input-Output Linearization --- p.130 / Chapter 6.5 --- MRAFC with Input-Output Linearization --- p.132 / Chapter 6.6 --- Example --- p.136 / Chapter 7 --- Analysis of MRAFC System --- p.140 / Chapter 7.1 --- Averaging technique --- p.140 / Chapter 7.2 --- Parameter convergence --- p.143 / Chapter 7.3 --- Robustness --- p.152 / Chapter 7.4 --- Simulation --- p.157 / Chapter 8 --- Application of MRAFC scheme on Manipulator Control --- p.166 / Chapter 8.1 --- Introduction --- p.166 / Chapter 8.2 --- Robot Manipulator Control --- p.170 / Chapter 8.3 --- MRAFC on Robot Manipulator Control --- p.173 / Chapter 8.3.1 --- Part A: Nonlinear-function feedback fuzzy controller --- p.174 / Chapter 8.3.2 --- Part B: State-feedback fuzzy controller --- p.182 / Chapter 8.4 --- Simulation --- p.186 / Chapter 9 --- Conclusion --- p.199 / Chapter A --- Implementation of MRAFC Scheme with Practical Issues --- p.203 / Chapter A.1 --- Rule Generation by MRAFC scheme --- p.203 / Chapter A.2 --- Implementation Considerations --- p.211 / Chapter A.3 --- MRAFC System Design Procedure --- p.215 / Bibliography --- p.217

by So Yuen Tai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves [144]-[146]. / ACKNOWLEDGEMENTS / TABLE OF CONTENTS --- p.T.1 / ABSTRACT / Chapter 1. --- INTRODUCTION --- p.1.1 / Chapter 1.1 --- Inexact Knowledge in Medical Expert Systems --- p.1.1 / Chapter 1.2 --- Fuzzy Expert System Shells --- p.1.2 / Chapter 1.2.1 --- SPII-2 --- p.1.3 / Chapter 1.2.2 --- Fuzzy Expert System Shell for Decision Support System --- p.1.4 / Chapter 1.3 --- Medical Expert Systems --- p.1.6 / Chapter 1.3.1 --- EXPERT --- p.1.6 / Chapter 1.3.2 --- DIABETO --- p.1.8 / Chapter 1.4 --- Impact from Micro-computer --- p.1.10 / Chapter 1.5 --- Approach --- p.1.11 / Chapter 2. --- SYSTEM Z-ll --- p.2.1 / Chapter 2.1 --- General Description --- p.2.1 / Chapter 2.2 --- Main Features --- p.2.2 / Chapter 2.2.1 --- Fuzzy Concepts --- p.2.2 / Chapter 2.2.2 --- Fuzzy Certainty --- p.2.3 / Chapter 2.2.3 --- Fuzzy Comparison --- p.2.5 / Chapter 2.2.4 --- Rule Evaluation --- p.2.7 / Chapter 2.2.5 --- Certainty Factor Propagation --- p.2.9 / Chapter 2.2.6 --- Linguistic Approximation --- p.2.10 / Chapter 2.3 --- Limitations and Possible Improvements --- p.2.11 / Chapter 3. --- A FUZZY EXPERT SYSTEM SHELL (Z-lll) IN PC ENVIRONMENT --- p.3.1 / Chapter 3.1 --- General Description --- p.3.1 / Chapter 3.2 --- Programming Environment --- p.3.1 / Chapter 3.3 --- Main Features and Structure --- p.3.3 / Chapter 3.3.1 --- Knowledge Acquisition Module --- p.3.5 / Chapter 3.3.1.1 --- Object Management Module --- p.3.5 / Chapter 3.3.1.2 --- Rule Management Module --- p.3.6 / Chapter 3.3.1.3 --- Fuzzy Term Management Module --- p.3.7 / Chapter 3.3.2 --- Consultation Module --- p.3.8 / Chapter 3.3.2.1 --- Fuzzy Inference Engine --- p.3.8 / Chapter 3.3.2.2 --- Review Management Module --- p.3.11 / Chapter 3.3.2.3 --- Linguistic Approximation Module --- p.3.11 / Chapter 3.3.3 --- System Properties Management Module --- p.3.13 / Chapter 3.4 --- Additional Features --- p.3 14 / Chapter 3.4.1 --- Weights --- p.3.15 / Chapter 3.4.1.1 --- Fuzzy Weight --- p.3.16 / Chapter 3.4.1.2 --- Fuzzy Weight Evaluation --- p.3.17 / Chapter 3.4.1.3 --- Results of Adding Fuzzy Weights --- p.3.21 / Chapter 3.4.2 --- Fuzzy Matching --- p.3.24 / Chapter 3.4.2.1 --- Similarity --- p.3.25 / Chapter 3.4.2.2 --- Evaluation of Similarity measure --- p.3.26 / Chapter 3.4.3 --- Use of System Threshold --- p.3.30 / Chapter 3.4.4 --- Use of Threshold Expression --- p.3.33 / Chapter 3.4.5 --- Playback File --- p.3.35 / Chapter 3.4.6 --- Database retrieval --- p.3.37 / Chapter 3.4.7 --- Numeric Variable Objects --- p.3.39 / Chapter 3.5 --- Implementation Highlights --- p.3.41 / Chapter 3.5.1 --- Knowledge Base --- p.4.42 / Chapter 3.5.1.1 --- Fuzzy Type --- p.4.42 / Chapter 3.5.1.2 --- Objects --- p.3.45 / Chapter 3.5.1.3 --- Rules --- p.3.49 / Chapter 3.5.2 --- System Properties --- p.3.53 / Chapter 3.5.2.1 --- System Menu --- p.3.53 / Chapter 3.5.2.2 --- Option Menu --- p.3.54 / Chapter 3.5.3 --- Consultation System --- p.3.55 / Chapter 3.5.3.1 --- Inference Engine --- p.3.56 / Chapter 3.5.3.2 --- Review Management --- p.3.60 / Chapter 3.6 --- Comparison on Z-lll and Z-ll --- p.3.61 / Chapter 3.6.1 --- Response Time --- p.3.62 / Chapter 3.6.2 --- Accessibility --- p.3.62 / Chapter 3.6.3 --- Accommodation of Large Knowledge Base --- p.3.62 / Chapter 3.6.4 --- User-Friendliness --- p.3.63 / Chapter 3.7 --- General Comments on Z-lll --- p.3.64 / Chapter 3.7.1 --- Adaptability --- p.3.64 / Chapter 3.7.2 --- Adequacy --- p.3.64 / Chapter 3.7.3 --- Applicability --- p.3.65 / Chapter 3.7.4 --- Availability --- p.3.65 / Chapter 4. --- KNOWLEDGE ENGINEERING --- p.4.1 / Chapter 4.1 --- Techniques used in Knowledge Acquisition --- p.4.1 / Chapter 4.2 --- Interviewing the Expert --- p.4.2 / Chapter 4.3 --- Knowledge Representation --- p.4.4 / Chapter 4.4 --- Development Approach --- p.4.6 / Chapter 4.5 --- Knowledge Refinement --- p.4.7 / Chapter 4.6 --- Consistency Check and Completeness Check --- p.4.12 / Chapter 4.6.1 --- The Consistency and Completeness in a nonfuzzy rule set --- p.4.13 / Chapter 4.6.1.1 --- Inconsistency in nonfuzzy rule-based system --- p.4.13 / Chapter 4.6.1.2 --- Incompleteness in nonfuzzy rule-based system --- p.4.18 / Chapter 4.6.2 --- Consistency Checks in Fuzzy Environment --- p.4.20 / Chapter 4.6.2.1 --- Affinity --- p.4.21 / Chapter 4.6.2.2 --- Detection of Inconsistency and Incompleteness in Fuzzy Environment --- p.4.24 / Chapter 4.6.3 --- Algorithm for Checking Consistency --- p.4.25 / Chapter 5. --- FUZZY MEDICAL EXPERT SYSTEMS --- p.5.1 / Chapter 5.1 --- ABVAB --- p.5.1 / Chapter 5.1.1 --- General Description --- p.5.1 / Chapter 5.1.2 --- Development of ABVAB --- p.5.2 / Chapter 5.1.3 --- Computerisation of Database --- p.5.4 / Chapter 5.1.4 --- Results of ABVAB --- p.5.7 / Chapter 5.1.5 --- From Minicomputer to PC --- p.5.15 / Chapter 5.2 --- INDUCE36 --- p.5.17 / Chapter 5.2.1 --- General Description --- p.5.17 / Chapter 5.2.2 --- Verification of INDUCE36 --- p.5.18 / Chapter 5.2.3 --- Results --- p.5.19 / Chapter 5.3 --- ESROM --- p.5.21 / Chapter 5.3.1 --- General Description --- p.5.21 / Chapter 5.3.2 --- Multi-layer Medical Expert System --- p.5.22 / Chapter 5.3.3 --- Results --- p.5.25 / Chapter 6. --- CONCLUSION --- p.6.1 / REFERENCES --- p.R.1 / APPENDIX I --- p.A.1 / APPENDIX II --- p.A.2 / APPENDIX III --- p.A.3 / APPENDIX IV --- p.A.14

by Lam Sze-sing. / "April 2000." / Thesis (Ph.D.)--Chinese University of Hong kong, 2000. / Includes bibliographical references 9p. 149-157). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.