Abstract
In order to predict the speed of ships, an prediction model is proposed according to the principal component analysis and BP neural network. Aimed at Changhang Yangshan ship 2, five main factors affecting speed are extracted through principal component analysis (PCA). Furthermore, the input and output of the BP neural network is designed by five main factors and predicted speed respectively. The initial parameters are selected to reduce the mean square error and the prediction error of the model. Finally, the prediction results show that the real speed is consistent with the predicted speed in trend, and the prediction accuracy is accurate. An effective way of speed prediction is provided by principal component analysis and BP neural network .
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This work is supported by China Ship Scientific Research Center.
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Xu, W., Li, Z., Hu, Q., Zhao, C., Zhou, H. (2022). Research on Ship Speed Prediction Model Based on BP Neural Network. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1566. Springer, Singapore. https://doi.org/10.1007/978-981-19-1253-5_31
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DOI: https://doi.org/10.1007/978-981-19-1253-5_31
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