Thesis (Ph. D.)--University of Alabama at Birmingham, 2007. / Print out. Additional advisors: James J. Buckley, Jeffrey G. Gray, Robert M. Hyatt, Randy K. Smith. Includes bibliographical references (leaves 185-207). Also available via the World Wide Web.

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 136-138).

M.Ing. (Electrical & Electronic Engineering) / Please refer to full text to view abstract

by Lee Wai Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 118-[124]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Review of Previous Work --- p.3 / Chapter 1.3 --- Scope of the Thesis --- p.4 / Chapter 2 --- Background Knowledge of Fuzzy Control System --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Fuzzy Sets --- p.7 / Chapter 2.2.1 --- Properties of Fuzzy Sets --- p.10 / Chapter 2.2.2 --- Operations on Fuzzy Sets --- p.13 / Chapter 2.3 --- Fuzzy Models --- p.14 / Chapter 2.3.1 --- Linguistic Model --- p.15 / Chapter 2.3.2 --- Takagi-Sugeno-Kang (TSK) Fuzzy Model --- p.16 / Chapter 2.4 --- Fuzzy Inference System --- p.17 / Chapter 2.4.1 --- Fuzzifier --- p.18 / Chapter 2.4.2 --- Knowledge Base --- p.19 / Chapter 2.4.3 --- Inference Engine --- p.19 / Chapter 2.4.4 --- Defuzzifier --- p.20 / Chapter 2.4.5 --- Product-Sum-Gravity Inference --- p.21 / Chapter 3 --- Decomposition of Fuzzy Rules --- p.25 / Chapter 3.1 --- Introduction --- p.25 / Chapter 3.2 --- Decomposability of Fuzzy Inference System --- p.26 / Chapter 3.3 --- The Decomposability condition --- p.29 / Chapter 3.4 --- Determining Decomposed Parameters --- p.32 / Chapter 3.5 --- Decomposable Approximation --- p.35 / Chapter 3.5.1 --- Linear Approximation --- p.38 / Chapter 3.5.2 --- Case Study --- p.40 / Chapter 3.6 --- Limitation of Decomposable Approximation --- p.42 / Chapter 3.7 --- Approximation Index --- p.44 / Chapter 3.7.1 --- Case Study --- p.48 / Chapter 3.8 --- Decomposable TSK Model --- p.52 / Chapter 3.8.1 --- Case Study --- p.54 / Chapter 3.9 --- Conclusion --- p.56 / Chapter 4 --- Fuzzy Identification --- p.58 / Chapter 4.1 --- Introduction --- p.58 / Chapter 4.2 --- Least-squares Estimation --- p.59 / Chapter 4.3 --- LSE Formulation of Various Fuzzy Models --- p.63 / Chapter 4.3.1 --- Linguistic Model --- p.63 / Chapter 4.3.2 --- TSK Model --- p.69 / Chapter 4.3.3 --- Decomposable System --- p.75 / Chapter 4.3.4 --- Comparative Case Study --- p.79 / Chapter 4.4 --- Fuzzy Regional System Identification --- p.81 / Chapter 4.4.1 --- Case Study --- p.86 / Chapter 4.5 --- Recursive Estimation --- p.86 / Chapter 4.5.1 --- Case Study --- p.90 / Chapter 4.6 --- Conclusion --- p.90 / Chapter 5 --- Performance-Based Fuzzy Gain Controller --- p.92 / Chapter 5.1 --- Introduction --- p.92 / Chapter 5.2 --- Conventional Fuzzy Control --- p.93 / Chapter 5.3 --- Fuzzy Gain Control --- p.95 / Chapter 5.4 --- Design Algorithm --- p.97 / Chapter 5.5 --- Stability Design Approach --- p.98 / Chapter 5.6 --- Simulation Case Study --- p.102 / Chapter 5.7 --- Conclusion --- p.106 / Chapter 6 --- Identification/Control Design Example --- p.107 / Chapter 7 --- Conclusion --- p.115 / Bibliography --- p.118

The idea for this research work is to use the fuzzy logic as a novel technique for modelling infantry tactics and doctrine that are currently being documented in flow charts. In flow charts we can only have yes-no decisions where one query branches either to another query (if the answer is no) or branches to an action (if the answer is yes). In such methods there are no various degrees of reasoning, just crisp yes or crisp no. This is not the way that humans usually reason. Hence, the introduction of fuzzy logic gives more flexibility in modelling human decision making process. / On the other hand, the knowledge-based systems are designed to mimic the performance of a human expert by transferring his/her expertise in a specific field to a computer-based model most oftenly in the form of a software package. This knowledge is often imprecise, or not all facts are available. Still humans are capable of making good decisions within such uncertain environment. / It was decided to use fuzzy logic for modelling infantry tactics and doctrine because fuzzy logic provides a basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning and decision making in such incompletely defined systems. Therefore the fuzzy logic seems a suitable choice in modelling infantry tactics and doctrine. / In this research work we first show how selected infantry tactics could be modelled utilising MATLAB® Fuzzy Toolbox. Then we develop a software package in Java programming language, which is then used to model the same infantry tactics. Using Java is necessary as the final aim of this project is to implement the developed models in the intelligent agent software (Jack). / The first part of this thesis is an overview of the fuzzy logic as one of the artificial intelligence paradigms. This part also briefly introduces intelligent agents, what they are and what they are not. The second part of this thesis shows the implementation of fuzzy reasoning in modelling selected infantry tactics and doctrine. The simulation results from all applications are also presented. / Thesis MEng(ElectronicEngineering byResearch)--University of South Australia, 2055.

Thesis (Ph. D.)--University of Washington, 1995. / Vita. Includes bibliographical references (leaves [133]-136).

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004. / Includes bibliographical references (leaves 48-52). Also available in electronic version. Access restricted to campus users.

Thesis (M.S.)--West Virginia University, 1999. / Title from document title page. Document formatted into pages; contains v, 134 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 66-69).

Thesis (M.Sc.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on July 7, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering and Intelligent Systems, [Department of] Electrical and Computer Engineering, University of Alberta. Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 62). Also available in electronic version.