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 used the FLEXFIS and eTS approaches as learning engines for evolving fuzzy (regression) models and compared them with a property valuating method employed by professional appraisers in reality as well as with a classical genetic fuzzy system. The comparison was based on a real-world data set taken from a cadastral system including prices of residential premises within the years 1998 to 2008.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 9th international conference on Fuzzy logic and applications |
| Pages | 123-130 |
| 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