In this thesis we explore prototype theory and random set theory as a foundation for antecedent labels in rule-based systems. In particular, we introduce three learning algorithms for regression problems and we also investigate the use of genetic algorithm as a generic parallleter optimization method for each of these approaches. Two of the learning algorithms generate Takagi-Sugeno rules whilst the third corresponds to a form of regression tree. Within the rules fuzzy labels are represented either through a combination of random set and prototype theory or through a novel extension of prototype theory to allow for fuzzy prototypes. Through out the thesis the algorithms are assessed on a number of benchmark data sets and compared with several state-of-the-art regression methods.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:681553 |
| Date | January 2014 |
| Creators | Li, Guanyi |
| Publisher | University of Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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