@inproceedings{faf824603d434045a1d28d8f2b3a59b1,
title = "Based on normal cloud model and d-s evidence theory comprehensive evaluation of operation state of fire control system",
abstract = "The characteristic information of fire control system is miscellaneous, which reflects the operation status of fire control system from different levels. However, due to the inaccuracy of measurement process, the evaluation criteria are not uniform.This will cause great ambiguity and uncertainty for real-time control of the health state of the fire control system.The traditional fuzzy comprehensive evaluation model only considers the fuzziness of the index but ignores the randomness. The cloud model theory considers both the fuzziness and the randomness of things. The model is applied to the fire control system. The analytic hierarchy process (AHP) is applied to obtain the correlation degree between the index and the operation level.In the overall evaluation of fire control system, the weight of feature state information is obtained by the method based on evidence trust degree, which avoids the influence of subjective weight on the overall evaluation results.D-S evidence theory is used to evaluate the overall operation status of fire control system.The validity of this method for real-time health control of fire control system is verified by an example.",
keywords = "D-S evidential theory, Fire control system, Normal cloud model style, Reliability of evidence, State assessment",
author = "Yingshun Li and Aina Wang and Xiaojian Yi",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 ; Conference date: 15-08-2019 Through 17-08-2019",
year = "2019",
month = aug,
doi = "10.1109/SDPC.2019.00167",
language = "English",
series = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "877--883",
editor = "Chuan Li and Shaohui Zhang and Jianyu Long and Diego Cabrera and Ping Ding",
booktitle = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
address = "United States",
}