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6.5.2 Real‐Time Event Risk Assessment (RTERA) Using Artificial Neural Networks
Author(s) -
Gronlund R.B.,
Simcock A.L.
Publication year - 2001
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2001.tb02371.x
Subject(s) - relation (database) , event (particle physics) , artificial neural network , computer science , operations research , risk analysis (engineering) , artificial intelligence , engineering , data mining , medicine , physics , quantum mechanics
This paper describes a novel technique that is being developed to create a decision aid for Real‐Time Event Risk Assessment (RTERA). RTERA allows the user to enter Real‐Time Events (RTEs) during a mission to identify and prioritise risks in relation to the mission. This decision aid uses Conventional Programming Techniques that use an Algorithmic approach to reach a specific result and also use Artificial Neural Networks (ANNs) to interpret RTEs and produce relative risks to the mission. Information on the current mission is entered, including which systems are to be used, the area of operation and the environmental conditions. The RTERA ‘user’ will be able to choose from a list of RTEs that will be determined during the development phase by a risk analysis of the platform/mission combination. The model then uses this information to determine a list of risk factors and their priorities in relation to the mission. The use of ANNs and Conventional Programming Techniques allows for previously undefined missions to be entered and appropriate risks to be displayed.