The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, fuzzy implication operators, and the fuzzy relation matrix, that are usually decided upon subjectively by an expert operator. The purpose of this dissertation is to develop an intelligent fuzzy control system that combines fuzzy controller and learning mechanism in a hybrid system. Such hybrid system, which allows for imprecise information and/or uncertain environments, is imperative to the process of developing effective robust control systems for a large number of important real-time industrial processes. It is shown in this dissertation that the performance of fuzzy control systems can be improved considerably if the fuzzy reasoning model is supplemented by learning mechanisms. Two learning mechanisms are proposed in this research: one that uses genetic algorithms and the other is based on the utilization of neural networks. The genetic algorithm enables us to generate an optimal set of parameters for the fuzzy reasoning model based on their initial subjective selection. The exploitation of this initial selection, i.e., knowledge of the domain, by the genetic algorithm leads to an improved performance of the fuzzy controller. The neural-fuzzy reasoning model combines the computational paradigms of neural network and fuzzy rule-based reasoning in a hybrid system that also leads to an improved performance of the fuzzy control system. / Source: Dissertation Abstracts International, Volume: 53-07, Section: B, page: 3605. / Major Professor: Abraham Kandel. / Thesis (Ph.D.)--The Florida State University, 1992.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_76695 |
| Contributors | Park, Daihee., Florida State University |
| Source Sets | Florida State University |
| Language | English |
| Detected Language | English |
| Type | Text |
| Format | 123 p. |
| Rights | On campus use only. |
| Relation | Dissertation Abstracts International |
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