Abstract
Fuzzy Cognitive Maps are network-like decision support tools, where the final conclusion is determined by an iteration process. Although the final conclusion relies on the assumption that the iteration reaches a fixed point, it is not straightforward that the iteration will converge to anywhere, since it can produce limit cycles or chaotic behaviour also. In this paper, we briefly analyse the behaviour of the so-called rescaled algorithm for fuzzy cognitive maps with respect to the existence and uniqueness of fixed points.
| Original language | English |
|---|---|
| Title of host publication | Studies in Computational Intelligence |
| Publisher | Springer Verlag |
| Pages | 43-49 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - Jan 1 2020 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 819 |
| ISSN (Print) | 1860-949X |
Keywords
- Fixed point
- Fuzzy cognitive map
- Rescaled algorithm
- Stability
ASJC Scopus subject areas
- Artificial Intelligence
Cite this
Notes on the rescaled algorithm for fuzzy cognitive maps. / Harmati, István; Kóczy, L.
Studies in Computational Intelligence. Springer Verlag, 2020. p. 43-49 (Studies in Computational Intelligence; Vol. 819).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Notes on the rescaled algorithm for fuzzy cognitive maps
AU - Harmati, István
AU - Kóczy, L.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Fuzzy Cognitive Maps are network-like decision support tools, where the final conclusion is determined by an iteration process. Although the final conclusion relies on the assumption that the iteration reaches a fixed point, it is not straightforward that the iteration will converge to anywhere, since it can produce limit cycles or chaotic behaviour also. In this paper, we briefly analyse the behaviour of the so-called rescaled algorithm for fuzzy cognitive maps with respect to the existence and uniqueness of fixed points.
AB - Fuzzy Cognitive Maps are network-like decision support tools, where the final conclusion is determined by an iteration process. Although the final conclusion relies on the assumption that the iteration reaches a fixed point, it is not straightforward that the iteration will converge to anywhere, since it can produce limit cycles or chaotic behaviour also. In this paper, we briefly analyse the behaviour of the so-called rescaled algorithm for fuzzy cognitive maps with respect to the existence and uniqueness of fixed points.
KW - Fixed point
KW - Fuzzy cognitive map
KW - Rescaled algorithm
KW - Stability
UR - http://www.scopus.com/inward/record.url?scp=85066118634&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066118634&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-16024-1_6
DO - 10.1007/978-3-030-16024-1_6
M3 - Chapter
AN - SCOPUS:85066118634
T3 - Studies in Computational Intelligence
SP - 43
EP - 49
BT - Studies in Computational Intelligence
PB - Springer Verlag
ER -