by Lam Wai. / Thesis (M.Ph.)--Chinese University of Hong Kong, 1988. / Bibliography: leaves 216-220.

by Yeung Wai Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 92-[96]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Fuzzy Inference and Real Intelligent Mapping --- p.1 / Chapter 1.2 --- Organization of the thesis --- p.5 / Chapter 2 --- Fuzzy Logic inference --- p.7 / Chapter 2.1 --- Fuzzy logic --- p.7 / Chapter 2.1.1 --- Fuzzy sets --- p.7 / Chapter 2.1.2 --- Operations on fuzzy sets --- p.10 / Chapter 2.2 --- Fuzzy Inference --- p.11 / Chapter 3 --- Weaknesses of fuzzy inference --- p.17 / Chapter 3.1 --- Is the use of linguistic fuzzy if-then rules and membership func- tions a good means of representing human expert knowledge? --- p.17 / Chapter 3.2 --- Role of conventional fuzzy inference doubtful if the expert knowl- edge is in the form of sampled input-output data --- p.21 / Chapter 3.3 --- Computational requirements --- p.23 / Chapter 3.4 --- Low transparency --- p.24 / Chapter 3.5 --- Analytical difficulties --- p.25 / Chapter 4 --- Real Intelligent Mapping --- p.27 / Chapter 5 --- Design of Real Intelligent Mapping Systems Using Dirichlet Tessellation --- p.33 / Chapter 5.1 --- Dirichlet tessellation for function approximation --- p.34 / Chapter 5.2 --- Identification of a DT based RIM system by least-squares --- p.42 / Chapter 5.3 --- Examples --- p.48 / Chapter 5.3.1 --- Defining the problem --- p.48 / Chapter 5.3.2 --- Balancing an inverted pendulum --- p.49 / Chapter 5.3.3 --- Balancing an inverted pendulum with cart --- p.53 / Chapter 5.3.4 --- Truck backing-up --- p.56 / Chapter 5.3.5 --- Chaotic time series prediction --- p.60 / Chapter 5.4 --- Interactive CAD platform for RIM systems design --- p.63 / Chapter 6 --- Analysis of Dirichlet tessellation based Real Intelligent Mapping Systems --- p.67 / Chapter 6.1 --- Local Stability Analysis of DT Based RIM Systems --- p.69 / Chapter 6.1.1 --- Balancing an inverted pendulum --- p.71 / Chapter 6.1.2 --- Truck backing-up --- p.73 / Chapter 6.2 --- Global stability analysis of DT based RIM systems --- p.74 / Chapter 6.3 --- Design of a stable DT based RIM system --- p.79 / Chapter 6.4 --- A method for analyzing Second order DT based RIM systems --- p.82 / Chapter 6.5 --- Piecewise-polynomial real domain representation of a class of fuzzy controller and its stability --- p.85 / Chapter 7 --- Conclusion --- p.90 / Bibliography --- p.92

by Kwok-leung Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 60-63). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Model --- p.7 / Chapter 2.1 --- Basic Beliefs on the vaguely classified variable --- p.7 / Chapter 2.2 --- Properties of the contingency table --- p.13 / Chapter 2.3 --- Mathematical model --- p.21 / Chapter Chapter 3 --- Simulation --- p.32 / Chapter 3.1 --- Likelihood function for the model and the simulation method --- p.32 / Chapter 3.2 --- Simulation result --- p.48 / Chapter Chapter 4 --- Discussion --- p.57 / Reference --- p.60

Au Yeung Ching Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 97-110). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Semantic Web and Ontologies --- p.3 / Chapter 1.2 --- Motivations --- p.5 / Chapter 1.2.1 --- Fuzziness of Concepts --- p.6 / Chapter 1.2.2 --- Typicality of Objects --- p.6 / Chapter 1.2.3 --- Context and Its Effect on Reasoning --- p.8 / Chapter 1.3 --- Objectives --- p.9 / Chapter 1.4 --- Contributions --- p.10 / Chapter 1.5 --- Structure of the Thesis --- p.11 / Chapter 2 --- Background Study --- p.13 / Chapter 2.1 --- The Semantic Web --- p.14 / Chapter 2.2 --- Ontologies --- p.16 / Chapter 2.3 --- Description Logics --- p.20 / Chapter 2.4 --- Fuzzy Set Theory --- p.23 / Chapter 2.5 --- Concepts and Categorization in Cognitive Psychology --- p.25 / Chapter 2.5.1 --- Theory of Concepts --- p.26 / Chapter 2.5.2 --- Goodness of Example versus Degree of Typicality --- p.28 / Chapter 2.5.3 --- Similarity between Concepts --- p.29 / Chapter 2.5.4 --- Context and Context Effects --- p.31 / Chapter 2.6 --- Handling of Uncertainty in Ontologies and Description Logics --- p.33 / Chapter 2.7 --- Typicality in Models for Knowledge Representation --- p.35 / Chapter 2.8 --- Semantic Similarity in Ontologies and the Semantic Web --- p.39 / Chapter 2.9 --- Contextual Reasoning --- p.41 / Chapter 3 --- A Formal Model of Ontology --- p.44 / Chapter 3.1 --- Rationale --- p.45 / Chapter 3.2 --- Concepts --- p.47 / Chapter 3.3 --- Characteristic Vector and Property Vector --- p.47 / Chapter 3.4 --- Subsumption of Concepts --- p.49 / Chapter 3.5 --- Likeliness of an Individual in a Concept --- p.51 / Chapter 3.6 --- Prototype Vector and Typicality --- p.54 / Chapter 3.7 --- An Example --- p.59 / Chapter 3.8 --- Similarity between Concepts --- p.61 / Chapter 3.9 --- Context and Contextualization of Ontology --- p.65 / Chapter 3.9.1 --- Formal Definitions --- p.67 / Chapter 3.9.2 --- Contextualization of an Ontology --- p.69 / Chapter 3.9.3 --- "Contextualized Subsumption Relations, Likeliness, Typicality and Similarity" --- p.71 / Chapter 4 --- Discussions and Analysis --- p.73 / Chapter 4.1 --- Properties of the Formal Model for Fuzzy Ontologies --- p.73 / Chapter 4.2 --- Likeliness and Typicality --- p.78 / Chapter 4.3 --- Comparison between the Proposed Model and Related Works --- p.81 / Chapter 4.3.1 --- Comparison with Traditional Ontology Models --- p.81 / Chapter 4.3.2 --- Comparison with Fuzzy Ontologies and DLs --- p.82 / Chapter 4.3.3 --- Comparison with Ontologies modeling Typicality of Objects --- p.83 / Chapter 4.3.4 --- Comparison with Ontologies modeling Context --- p.84 / Chapter 4.3.5 --- Limitations of the Proposed Model --- p.85 / Chapter 4.4 --- "Significance of Modeling Likeliness, Typicality and Context in Ontologies" --- p.86 / Chapter 4.5 --- Potential Application of the Model --- p.88 / Chapter 4.5.1 --- Searching in the Semantic Web --- p.88 / Chapter 4.5.2 --- Benefits of the Formal Model of Ontology --- p.90 / Chapter 5 --- Conclusions and Future Work --- p.91 / Chapter 5.1 --- Conclusions --- p.91 / Chapter 5.2 --- Future Research Directions --- p.93 / Publications --- p.96 / Bibliography --- p.97

Yang, Fan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 100-106). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Literature review --- p.1 / Chapter 1.2 --- The main contribution of this thesis --- p.5 / Chapter 1.3 --- Relations between the above three models --- p.7 / Chapter 2 --- Model 1 --- p.9 / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.2 --- Minimax rule risk function --- p.11 / Chapter 2.3 --- Fuzzy liquidity of asset --- p.12 / Chapter 2.4 --- Notations --- p.15 / Chapter 2.5 --- Model formulation --- p.16 / Chapter 2.6 --- Numerical example and result --- p.25 / Chapter 3 --- Model 2 --- p.36 / Chapter 3.1 --- Introduction --- p.36 / Chapter 3.2 --- Notations --- p.39 / Chapter 3.3 --- Model formulation --- p.41 / Chapter 3.4 --- Numerical example and result --- p.45 / Chapter 4 --- Model 3 --- p.51 / Chapter 4.1 --- Introduction --- p.51 / Chapter 4.2 --- Notations --- p.52 / Chapter 4.3 --- Model formulation --- p.54 / Chapter 4.4 --- Numerical example and result --- p.62 / Chapter 5 --- Conclusion --- p.68 / Chapter A --- Source Data for Model 1 --- p.71 / Chapter B --- Source Data for Model 2 --- p.80 / Chapter C --- Source Data for Model 3 --- p.90 / Bibliography --- p.100

As the most essential feature in problem solving and decision making by humans, uncertainty information occur frequently in business, scientific and engineering disciplines. The explosive growth and diverse forms of uncertainty information in the stored data have generated an urgent requirement for new techniques and tools that can intelligently and automatically assist us in eliciting valuable knowledge from raw data. / The DCIFI is defined based on the Choquet extension of a signed fuzzy measure. A numerical calculation algorithm is implemented to derive the integration result of the DCIFI. A DCIFI regression model is designed to handle the regression problem where heterogeneous fuzzy data are involved. We propose a GA-based Double Optimization Algorithm (GDOA) to retrieve the internal coefficients of the DCIFI regression model. Besides that, A DCIFI projection classifier, which is capable of classifying heterogeneous fuzzy data efficiently and effectively, is established. We proposed a GA-based Classifier-learning Algorithm (GACA) to search the relevant internal parameters of the DCIFI projection classifier. Both the DCIFI regression model and projection classifier are very informative and powerful to deal with heterogeneous fuzzy data sets with strong interaction. Their performances are validated by a series of experiments both on synthetic and real data. (Abstract shortened by UMI.) / This thesis is mainly devoted to a comprehensive investigation on innovative data mining methodologies which merge the advantages of nonlinear integral (Choquet integral) in the representation of nonlinear relationship and fuzzy set theory in the description of uncertainty existed in practical data bases. It proposes two fuzzifications on the classical Choquet integral, one is the Defuzzified Choquet Integral with Fuzzy-valued Integrand (DCIFI), and the other is the Fuzzified Choquet Integral with Fuzzy-valued Integrand (FCIFI). The DCIFI and the FCIFI are regarded as generalizations of Choquet integral since both of them allow their integrands to be fuzzy-valued. The difference lies in that the DCIFI has its integration result non-fuzzified while the FCIFI has its integration result fuzzified. Due to the different forms of integration results, the DCIFI and the FCIFI have their distinct theoretic analyses, implementation algorithms, and application scopes, respectively. / by Rong Yang. / "April 2005." / Advisers: Kwong-Sak Leung; Pheng-Ann Heng. / Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0371. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 187-199). / 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, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 163-177).

Eigen fuzzy sets of fuzzy relation can be used for the estimation of highest and lowest levels of involved variables when applying max-min composition on fuzzy relations. By the greatest eigen fuzzy sets (set which can be greater anymore) maximum membership degrees of any fuzzy set can be found, with the help of least eigen fuzzy set (set which can be less anymore) minimum membership degrees of any fuzzy sets can be found as well.The lowest and highest level, impact or e ffect of anything can be found by applying eigen fuzzy set theory. The implicational aspect of this research study is medical and customer satisfaction level measurement. By applying methods of eigen fuzzy set theory the e ffectiveness of medical cure and customer satisfaction can be found with high precision.

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