Abstract：In order to solve the problem that the new inspection regime of the Tokyo MOU is difficult to identify the inspection order of the inspected ships, the method of principal component analysis and extreme learning machine was used to study the selection of the inspected ships. Based on the port state control data of the Tokyo MOU, the targeting model was analyzed in case study and the predicted results were compared with the actual data. The research results show that the proposed ship selection model is based on principal component analysis to reduce the dimension and complexity of the index samples and the extreme learning machine is used to fit and predict the detention and defects of the inspected ships, which can reduce the inspection volume by half with the accuracy rate of 90%. The validity of the proposed model is verified, which can provide decision support for the port state control for ship selection.
郑中义，牟家奇. 基于主成分分析-极限学习机的选船模型[J]. 重庆交通大学学报（自然科学版）, 2020, 39(12): 31-36.
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2020, Vol. 39 
