Complex fuzzy logic is a new type of multi-valued logic, in which truth values are drawn from the unit disc of the complex plane; it is thus a generalization of the familiar infinite-valued fuzzy logic. At the present time, all published research on complex fuzzy logic is theoretical in nature, with no practical applications demonstrated. The utility of complex fuzzy logic is thus still very debatable. In this thesis, the performance of ANCFIS is evaluated. ANCFIS is the first machine learning architecture to fully implement the ideas of complex fuzzy logic, and was designed to solve the important machine-learning problem of time-series forecasting. We then explore extensions to the ANCFIS architecture. The basic ANCFIS system uses batch (offline) learning, and was restricted to univariate time series prediction. We have developed both an online version of the univariate ANCFIS system, and a multivariate extension to the batch ANCFIS system. / Software Engineering and Intelligent Systems
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/891 |
| Date | 06 1900 |
| Creators | Sara, Aghakhani |
| Contributors | Dick, Scott (Electrical and Computer Engineering), Musilek, Petr (Electrical and Computer Engineering), Lu, Paul (Computing Science) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 2450451 bytes, application/pdf |
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