Modeling General Aviation Startle Causality using Fuzzy Cognitive Maps

EasyChair Preprint 802

Abstract

Current data in the literature, suggest that over the last decade, loss of control in-flight (LOC-I) account for over 40% of all fixed wing fatalities [38]. This issue of LOC is also reflected in UK based data on the subject of General Aviation (GA) accidents causality [23, 48]. As discussed in [24, 32, 38], the occurrence of upsets and LOC have predominantly been studied within the transport and commercial aircraft categories, FAA Title 14 Operations (Parts 121; 135), leaving the GA, Part 91 operations category, a lot less examined and relatively underde-veloped in comparison. This disparity motivates the current research in that, given the propensity of Part 91 rules to be an equally high-risk enterprise, it is worthy of careful consideration regarding LOC-I and upset prevention and recovery (UPR) research. This paper presents an overview of the FCM strategy, applied to the context of startle, their possible causes, and the potential impact on perfor-mance, as a holistic approach to understanding and mitigating, the challenge of startle potentiated loss of control.

Keyphrases: Causal factor, Evolutionary Algorithms, Fuzzy Cognitive Map, Fuzzy Cognitive Maps, Fuzzy Logic, General Aviation, General Aviation Safety, Hebbian learning, Human Factors, Pilot Training, Startle, adaptive algorithms, decision making, eye tracking, human factor, human performance model, learning algorithm, loss of control, simulation, startle causality, startle process

@booklet{EasyChair:802,
  author    = {Abiodun Yusuf and Ah-Lian Kor and Hissam Tawfik},
  title     = {Modeling General Aviation Startle Causality using Fuzzy Cognitive Maps},
  howpublished = {EasyChair Preprint 802},
  year      = {EasyChair, 2019}}