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| Throughput Prediction of Port Based on Back Propagation Neural Network Optimized by Ant Colony Algorithm |
| LI Chang-an1,3,4,LU Xue-qin2,WU Zhong-qiang2,ZHANG Li-jie1,3 |
1. Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of Education of China, Yanshan University, Qinhuangdao, Hebei 066004, China
2. College of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
3. Hebei Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao, Hebei 066004, China
4. Shenhua Tianjin Coal Terminal Co. Ltd., Tianjin 300457, China |
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Abstract Port cargo throughput is an important index of port production and operation scale, and it is the basis for port construction and development. In order to maximize the role of port, it is necessary to make a reasonable and effective forecast for port cargo throughput. Ant colony algorithm is used to optimize the initial weight and threshold of BP neural network, and the prediction model is established to predict the port cargo throughput. Ant colony algorithm has the characteristics of global search, distributed computation and strong robustness, which is beneficial to accelerate the convergence speed of BP neural network, avoids the problem of easy to fall into local extremum, and improves the modeling accuracy. The application in port throughput prediction shows that the average absolute percentage errors of BP neural network model optimized by ant colony algorithm, fuzzy neural network prediction model, RBF prediction model and BP prediction model are 2.826%, 3.734%, 4.990% and 6.566% respectively; meanwhile, the convergence speed of BP neural network model optimized by ant colony algorithm is the fastest.
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Received: 16 August 2019
Published: 02 November 2020
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2020, Vol. 41 
