高層気象パターンに基づくウェザールーティング : ニューラルネットワークによる航路選定

概要

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This paper describes a new approach to the strategic route selection problem at the departure time using neural network which is known as a powerful tool of pattern recognition. The 5-day mean 500hPa heights over the North Pacific Ocean for the first 5 days and the latter 5-days during the voyage were input to a neural network. The teacher signals were prepared by calculating the route scores (scores determined by passage times) of 18 routes from San Francisco to Tokyo. Learning of a neural network was performed for 105 voyages during 5 winter seasons. To verify the generalization ability, a new set of 5-day mean 500hPa heights in the different winter seasons were input to a trained neural network. As a result, compared with the recommended routes based on the zonal index, a trained neural network could provide much more advantageous routes.
社団法人日本航海学会の論文
1995-09-25