In this thesis, we propose an improved fuzzy GBML¡]genetic-based machine learning¡^algorithm to construct a FRBCS¡]fuzzy rule-based classification system¡^for pattern classification problem.
Existing hybrid fuzzy GBML algorithm is consuming more computational time since it used the SS fuzzy model and combined with the Michigan-style algorithm for increasing the convergent rate of the Pittsburgh-style algorithm. By contrast, our improved fuzzy GBML algorithm is consuming less computational time since it used the MW fuzzy model and instead of the role of the Michigan-style algorithm by a heuristic procedure. Experimental results show that improved fuzzy GBML algorithm possesses the shorter computational time, the faster convergent rate, and the slightly better classification rate.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0220106-160055 |
| Date | 20 February 2006 |
| Creators | Lee, Cheng-Tsung |
| Contributors | Tsung-Chuan Huang, Chih-Hung Wu, Shie-Jue Lee |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0220106-160055 |
| Rights | unrestricted, Copyright information available at source archive |
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