A Novel Approach to Fuzzy Cognitive Map Based on Hesitant Fuzzy Sets for Modeling Risk Impact on Electric Power System
- 10.2991/ijcis.d.190722.001How to use a DOI?
- Fuzzy cognitive map; Hesitant fuzzy set; Risk factors; Electric power system; Simulation
Electric power industry has been undergoing enormous transformations. Therefore, it is necessary to improve the security of electric power system and the decision capacity in the emergency process. As a complicated system, electric power system is affected by many factors, the reasoning of which can be regarded as a process that combines intuition and cognition. Uncertainty characterizes human cognitive and reasoning processes. Several extensions of fuzzy cognitive map (FCM) model have been suggested to handle multifarious sources of uncertainty. Nevertheless, the uncertainty from human doubt may arise in the assignment of membership degrees, which is neglected in current FCMs. To deal with this problem, a novel approach based on hesitant fuzzy sets (HFSs) and FCMs, called hesitant fuzzy cognitive maps (HFCMs), is presented in this paper. The proposed method, which possesses the features of tackling hesitancy explicitly during the experts' assessments, is demonstrated by an example on the analysis of risk factors affecting electric power system. It can provide a better simulation of the inherent uncertainty in real problems through the hesitancy represented in experts' knowledge, and a what-if analysis for describing hesitant fuzzy scenario is well developed with the HFCM model.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Xiaodi Liu AU - Zengwen Wang AU - Shitao Zhang AU - Jiashu Liu PY - 2019 DA - 2019/08/08 TI - A Novel Approach to Fuzzy Cognitive Map Based on Hesitant Fuzzy Sets for Modeling Risk Impact on Electric Power System JO - International Journal of Computational Intelligence Systems SP - 842 EP - 854 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.190722.001 DO - 10.2991/ijcis.d.190722.001 ID - Liu2019 ER -