Abstract
This paper presents a framework for studying the effectiveness of evolutionary strategies for generating fuzzy rule bases from training data. The fitness measure needed for selection is obtained by a comparison of the training data with the function approximation defined by a fuzzy rule base. The properties of employing both global and local fitness measures are examined. Rule base completion is obtained by incorporating a global evaluation of the smoothness of the transitions between local regions into the selection process.
| Original language | English |
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| Pages | 203-207 |
| Number of pages | 5 |
| DOIs | |
| State | Published - 1999 |
| Event | 1999 International Conference of the North American Fuzzy Information Processing Society - New York, NY, USA Duration: Jun 10 1999 → Jun 12 1999 Conference number: 18 |
Conference
| Conference | 1999 International Conference of the North American Fuzzy Information Processing Society |
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| Abbreviated title | NAFIPS'99 |
| City | New York, NY, USA |
| Period | 6/10/99 → 6/12/99 |
ASJC Scopus Subject Areas
- General Computer Science
- General Mathematics
Keywords
- Algorithm design and analysis
- Clustering algorithms
- Computer science
- Data analysis
- Fuzzy sets
- Marine vehicles
- Quantization
- Takagi-Sugeno-Kang model
- Training data
Disciplines
- Computer Sciences
- Engineering
- Mathematics