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.

by Chen, Zhong-Yu. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (p. 146-[154]). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

Recent technical progress has made new forms of controller implementations on computers possible. Especially the technique of Fuzzy Logic Control has found a growing number of applications. Also the development of fast A/D converters has made the acquisation of data with vision based systems possible. In this project we combine the two techniques of Fuzzy Logic Control and Vision Feedback to control an inverted pendulum and to determine their usefulness and limitations. The experiment was conducted and provided us with the data necessary to judge the performance of the new control strategy. The gathered data support the hypothesis that it is possible to control the inverted pendulum with Fuzzy Logic Control using Vision Feedback, though not without limitations. / Graduation date: 1995

Thesis (Ph. D.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 187-198).

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 18, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Thesis advisor: Dr. Marjorie Skubic. Includes bibliographical references.

Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 524-542).

published_or_final_version / abstract / toc / Electrical and Electronic Engineering / Master / Master of Philosophy