Study on the Evaluation of UAV Disaster Monitoring System Architecture based on the RSBFNN Algorithmic Method

Authors

Zhifei Li, College of Information Systems and Management, National University of Defense Technology, 410073 Changsha, Hunan, P. R. ChinaFollow
Yifan Zhu, College of Information Systems and Management, National University of Defense Technology, 410073 Changsha, Hunan, P. R. ChinaFollow
Feng Yang, College of Information Systems and Management, National University of Defense Technology, 410073 Changsha, Hunan, P. R. ChinaFollow
Wenguang Wang, College of Information Systems and Management, National University of Defense Technology, 410073 Changsha, Hunan, P. R. ChinaFollow

Author Country (or Countries)

P. R. China

Abstract

In disaster monitoring, Unmanned Aerial Vehicles (UAVs) are becoming more suitable than earth observing satellites and manned helicopters, especially in emergent situations. However, decisions must be made as to which attributes the UAV must possess when developing new models. Combined with NOLH experiment design and multi-agent simulation (MAS) technology, a new architecture evaluation method, termed Rough Set Based Fuzzy Neural Network (RSBFNN), is proposed to simulate and analyze both the UAV attributes and desired effectiveness. Experimental results show that this method is very dependable when comparing predicted and actual responses and yields better performance than other models. This method best fits the design of the disaster monitoring UAV, and can be used to perform a series of dynamic trade studies, in which various architecture alternatives are examined and compared.

Recommended Citation

Li, Zhifei; Zhu, Yifan; Yang, Feng; and Wang, Wenguang (2015) "Study on the Evaluation of UAV Disaster Monitoring System Architecture based on the RSBFNN Algorithmic Method," Applied Mathematics & Information Sciences: Vol. 09: Iss. 3, Article 40.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol09/iss3/40