Abstract
In this paper, we investigate on-line fuzzy modeling for predicting
the prices of residential premises using the concept of evolving fuzzy models. These combine the aspects of incrementally updating the parameters and expanding the inner structure on demand with the concepts of uncertainty modeling in a possibilistic and linguistic manner (achieved through fuzzy sets and fuzzy rule bases). We use the FLEXFIS approach as learning engine for evolving fuzzy (regression) models, exploiting the Takagi-Sugeno fuzzy model architecture. The comparison with state-of-the-art expert-based premise estimation was based on a real-world data set including prices for residential premises within the years 1998 to 2008, and showed that FLEXFIS was able to out-perform expert-based method.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Hybrid Artificial Intelligence Systems Conference, HAIS 2011 |
| Pages | 107-115 |
| Number of pages | 8 |
| Publication status | Published - 2011 |
Fields of science
- 101001 Algebra
- 101 Mathematics
- 102 Computer Sciences
- 101013 Mathematical logic
- 101020 Technical mathematics
- 102001 Artificial intelligence
- 102003 Image processing
- 202027 Mechatronics
- 101019 Stochastics
- 211913 Quality assurance
JKU Focus areas
- Computation in Informatics and Mathematics
- Mechatronics and Information Processing
- Nano-, Bio- and Polymer-Systems: From Structure to Function