Applications of Contextual Fuzzy Operators and Fuzzy Extensions for Polymorphism, Data Mining and Analysis.

摘要

System behaviors and processes are often influenced to a large degree by external conditions. As those conditions change, so must any corresponding behavioral response. Driving a car from point A to point B when the road is straight and smooth is a dramatically different operation than if the surface is a sheet of ice with twists and turns. Even more challenging is when both surfaces are part of the same problem, say when a car must navigate a patch of "black ice" on the highway. Significant changes in the nature of a problem, such as when a surface changes from asphalt to ice, are called Situational Discontinuity Problems (SDPs) and present a distinct challenge to computer scientists and programmers.;First, behaviors which might be appropriate for past problem states are inappropriate for future states. Second, data discontinuities, make it difficult to describe all-encompassing actions without a "frame of reference". Even well-known problems such as simple noise and outliers pose problems for processes. Third is the general uncertainty surrounding SDPs in or near transition states where multiple approaches are equally desirable.;Traditional approaches to SDPs involve generalizing an existing algorithm. This has the undesired side-effect of greatly increasing complexity while also providing a diminishing ability to further extend the algorithm. Fuzzy Contexts, or Fuzzy Logic Type-C with Fuzzymorphism, is an architecture designed to allow process extensions to solve SDPs or other disparate problems while also minimizing complexity and diminishing returns.;This dissertation explores prior work and methods used to solve SDPs and their limitations. It then introduces the notion of Fuzzy Contexts and Fuzzymorphism in a novel software framework supported with database extensions, a database-driven and XML-based configuration language, combined with evolutionary and local search techniques. It further explores the use of the framework and the Type-C architecture in various processes from data mining to robotic control in order to improve upon existing techniques.