TY - GEN
T1 - Probabilistic fuzzy cognitive map
AU - Song, Heng-Jie
AU - Shen, Zhi-Qi
AU - Miao, Chun-Yan
AU - Liu, Zhi-Qiang
AU - Miao, Yuan
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2006
Y1 - 2006
N2 - In this paper, we present the Probabilistic Fuzzy Cognitive Map (PFCM) which is a novel extension of FCM theory. Each concept in PFCM is extended to a fuzzy event that models not only the fuzzy degree but also the fuzzy probability of both the cause and the effect concepts. PFCM enhances the capability of conventional FCMs to handle both randomness and fuzziness which are necessary to model the uncertainty involved in inference process of complex causal system. A formalized inference process of PFCMs is presented for adjustments on probability of fuzzy events and for dynamic update of causal weights. This enables PFCM to synthetically analyze the impacts of randomness and fuzziness on causal inference process. The simulation result shows a good match to the above features of PFCM. PFCM, as an initial attempt, provides a heuristic approach to model the uncertainty of complex causal systems and opens a collection of interesting research issues for further research. © 2006 IEEE.
AB - In this paper, we present the Probabilistic Fuzzy Cognitive Map (PFCM) which is a novel extension of FCM theory. Each concept in PFCM is extended to a fuzzy event that models not only the fuzzy degree but also the fuzzy probability of both the cause and the effect concepts. PFCM enhances the capability of conventional FCMs to handle both randomness and fuzziness which are necessary to model the uncertainty involved in inference process of complex causal system. A formalized inference process of PFCMs is presented for adjustments on probability of fuzzy events and for dynamic update of causal weights. This enables PFCM to synthetically analyze the impacts of randomness and fuzziness on causal inference process. The simulation result shows a good match to the above features of PFCM. PFCM, as an initial attempt, provides a heuristic approach to model the uncertainty of complex causal systems and opens a collection of interesting research issues for further research. © 2006 IEEE.
KW - Causal inference process
KW - Dynamic property
KW - Fuzzy cognitive map
KW - Fuzzy event
KW - Probabilistic fuzzy cognitive map
UR - http://www.scopus.com/inward/record.url?scp=34250729637&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-34250729637&origin=recordpage
U2 - 10.1109/FUZZY.2006.1681865
DO - 10.1109/FUZZY.2006.1681865
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780394887
SN - 9780780394889
T3 - IEEE International Conference on Fuzzy Systems
SP - 1221
EP - 1228
BT - 2006 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2006
PB - IEEE
T2 - 2006 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2006
Y2 - 16 July 2006 through 21 July 2006
ER -