A Basic Study on the Determination of Optimum Ship Route Using Neural Network

本文提出了一种基于神经网络的船舶天气航路定位方法，这是一种模式识别的有力工具。提出的天气路由神经网络由三层组成，即输入层、隐藏层和输出层。将航行前5天和后5天覆盖北太平洋网格点的5天平均500hPa高度输入到神经网络中。教师信号是通过模拟一艘集装箱船在从旧金山到东京的不同航线上的航行，使用分析的波浪数据并计算这些航线的通过时间来产生的。然后根据每条路线的通行时间计算得分，范围从0.1到0.9。得分最高的为0.9分，得分最低的为0.1分，得分最高的为最短时间路由，得分最低的为最长时间路由。这些分数被用作教师的信号。为了对所提出的神经网络进行学习，在网络中反复输入5个冬季（1978-1983）连续2个5天平均500hPa高度模式，修改隐含层和输出层各单元的权值和阈值，使网络输出信号与教学信号一致。学习完成后，将1989-1991年不同冬季连续2个5天平均500hPa高度模式输入网络，验证网络的有效性。结果，训练后的神经网络的输出信号与目标信号，即模拟计算出的航线分数，在大多数航次中都非常吻合。综上所述，在连续两个5天平均500hPa高度模式下，所提出的天气路线神经网络可以为大多数航次提供最优或次优路线。

In this research, a new method of ship weather routing was developed using the neural network which is known as a powerful tool of pattern recognition. The proposed weather routing neural network consists of three layrs, i.e.input, hidden and output layrs. The 5-day mean 500hPa heights on the grid points covering the North Pacific Ocean for the first 5 days and the latter 5 days during the voyage were input to the neural network.The teacher signals were produced by simulating the navigation of a container ship on the various routes from San Francisco to Tokyo using the analyzed wave data and calculating the passage times of these routes. A score of each route was then computed based on the passage time so as to range from 0.1 to 0.9. The highest score 0.9 and the lowest score 0.1 were allocated to the minimum time route and the maximum time route, respectively. These scores were used as the teacher signals.To perform the learning of the proposed neural network, many successive two 5-day mean 500hPa height patterns during 5 winter seasons (1978-1983) were input to the network repeatedly, and the weights and threshold of each unit of the hidden and output layrs were modified so as to let the output signals from the network coincide with the teaching signals. After the completion of the learning, a new set of successive two 5-day mean 500hPa height patterns in the different winter seasons (1989-1991) were input to the network to verify the effectiveness of the network.As a result, the output signals of a trained neural network coincided with the target signals, i.e.the scores of the routes calculated by the simulations, very well for most of the voyages. In conclusion, the proposed weather routing neural network could provide the optimum or sub-optimum routes for most of the voyages given the accurate successive two 5-day mean 500hPa height patterns.