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A Neuro-Fuzzy Hybridization Approach to Model Weather Operations in a Virtual Warfare Analysis System

Abstract : Weather operations play an important and integral part of planning, execution and sustainment of mission operations. In this paper, a neuro-fuzzy hybridization technique is applied to model the weather operations and predict its impact on the effectiveness of air tasking operations and missions. Spatio-temporal weather data from various meteorological sources are collected and used as the input to a neural network and the predicted weather conditions at a given place is classified based on fuzzy logic. The corresponding fuzzy rules are generated forming the basis for introducing the weather conditions in the evaluation of the effectiveness of the military mission plans. An agent-based architecture is proposed where agents representing the various weather sensors feed the weather data to the simulator, and a weather agent developed using neuro-fuzzy hybridization computes the weather conditions over the flight plan of the mission. These rules are then used by the Mission Planning and Execution system that evaluates the effectiveness of military missions in various weather conditions.
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D. Vijay Rao, Lazaros Iliadis, Stefanos Spartalis. A Neuro-Fuzzy Hybridization Approach to Model Weather Operations in a Virtual Warfare Analysis System. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.111-121, ⟨10.1007/978-3-642-23957-1_13⟩. ⟨hal-01571376⟩



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