Chinese Journal of Ship Research, Volume. 17, Issue 4, 98(2022)

Meteorological shipping route decision-making system based on BP neural network

Daheng ZHANG1,2, Yingjun ZHANG1, and Chuang ZHANG1
Author Affiliations
  • 1Navigation college, Dalian Maritime University, Dalian 116026, China
  • 2School of Navigation and Naval Architecture, Dalian Ocean University, Dalian 116023, China
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    Figures & Tables(15)
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    • Table 1. Ship parameters

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      Table 1. Ship parameters

      参数数值
      类型多用途船
      下水日期1995.11.25
      船长/m148.7
      型宽/m22.7
      型深/m10.2
      总吨/t12 335
      夏季载重/t5 700
      功率/kW6 480×2
      制动功率/kW5 184×2
    • Table 2. The range of input and output variables of the BP neural network

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      Table 2. The range of input and output variables of the BP neural network

      变量变量范围
      最小值最大值
      注:“−”表示不利于节省燃油。
      船速/kn14.318.5
      主机转速/(r·min−1)480510
      平均吃水/m5.56.2
      吃水差/m0.22.0
      货物重量/t313.1102 588.34
      风影响/kn[21]−39.035.0
      海浪影响/m[21]−7.06.0
      燃油消耗量/(t·h−1)0.772.52
    • Table 3. Weights and biases of the input layer and hidden layer

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      Table 3. Weights and biases of the input layer and hidden layer

      zc1ic2ic3ic4ic5ic6ic7iwib1ib2
      1−0.732 1−0.067 1−0.512 90.789 4−0.691 41.188 5−0.459 1−0.325 01.928 0−0.315 0
      2−0.516 21.023 60.501 70.686 90.748 61.503 00.041 90.418 9−1.319 8
      3−0.522 10.298 80.718 2−1.002 00.818 7−0.831 2−0.235 0−0.961 5−1.211 4
      40.988 81.412 00.694 10.573 1−0.612 30.400 20.945 1−0.398 70.821 6
      50.010 20.005 91.456 3−0.100 8−1.391 30.100 8−0.101 60.438 40.011 4
      60.271 61.356 00.711 2−0.891 50.519 8−0.085 2−0.291 50.598 1−0.138 1
      7−0.698 7−1.281 2−0.508 1−0.411 9−0.598 11.427 4−1.325 8−0.681 0−0.584 9
      8−0.416 61.258 9−1.524 00.499 10.358 9−1.698 7−1.028 80.496 91.301 8
      90.487 1−0.401 10.816 51.601 80.644 90.348 90.818 50.591 61.692 1
      10−0.609 8−0.498 70.319 80.998 7−0.401 21.158 5−0.801 20.511 8−2.107 8
    • Table 4. Fuel consumption statistics results

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      Table 4. Fuel consumption statistics results

      参数最大最小平均95%置信区间
      上限下限
      燃油消耗量/(t·h−1)2.5210.7681.3211.2581.384
    • Table 5. MSE and RMSE of BP neural network and MR

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      Table 5. MSE and RMSE of BP neural network and MR

      参数多元回归分析模型BP神经网络
      MSE0.032 00.021 0
      RMSE0.178 90.144 9
    • Table 6. Ship fuel consumption statistics

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      Table 6. Ship fuel consumption statistics

      航速/kn总航程/n mile航行时间/h平均燃油消耗量/(t·h−1)燃油消耗总量/t
      15412.4127.491.32736.48
      17426.5625.091.48437.24
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    Daheng ZHANG, Yingjun ZHANG, Chuang ZHANG. Meteorological shipping route decision-making system based on BP neural network[J]. Chinese Journal of Ship Research, 2022, 17(4): 98

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    Paper Information

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    Received: Oct. 25, 2021

    Accepted: --

    Published Online: Mar. 26, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.02565

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