基于混合模型EWT-PSO-SA-SVR的港口吞吐量区间预测 Alternative Title Interval Prediction of Container Throughput Based on A Novel Hybrid Model EWT-PSO-SA-SVR 徐晓梅 Thesis Advisor 牛明飞 Degree Name 硕士 2018-03-01 Keyword 经验小波变换 支持向量回归 粒子群优化算法 模拟退火算法 港口吞吐量 区间预测 Abstract 近年来，随着中国经济的不断繁荣，中国港口已经成为世界港口运输中的重要组成部分。港口集装箱吞吐量的准确预测对于未来港口的建造和升级都起着非常重要的作用。 本文重点对大连港和深圳港集装箱吞吐量数据进行分析，提出了混合模型EWT-PSO-SA-SVR并进行区间预测。首先，应用经验小波变换（EWT）对原始数据序列进行分解，得到一个分解序列集；其次，选用粒子群模拟退火优化算法（PSO- SA）优化支持向量回归（SVR）中的参数；最后，运用新提出的区间预测混合模型 EWT- PSO- SA- SVR对大连港和深圳港集装箱吞吐量数据进行了区间预测。在本文当中，也对新提出的组合模型与其他模型进行了比较，比较模型有 EWT- CPSO- SVR、 EWT- CS- SVR、 EWT- PSO- SVR和 EWT- FA- SVR。 实证结果表明，本文提出的混合模型在大连港和深圳港集装箱吞吐量数据上的预测性能优于其他比较模型。 Other Abstract In recent years,with the continuous prosperity of Chinese economy, Chinese ports have become an important part of the world’s port transportation system. The accurate predicition of  container throughput is very important for the construction and upgrading of ports. This paper focuses on the analysis of container throughput data of Dalian Port and Shenzhen Port, and proposes the hybrid model EWT-PSO-SA-SVR to perform interval forecasting. Firstly, the original data sequence is decomposed by an Empirical Wavelet Transform (EWT) algorithm to obtain a set of decomposed sub-sequences. Secondly, the Particle Swarm-Simulated Annealing Optimization algorithm was employed to optimize the parameters of Support Vector Regression (SVR). Finally, this paper use the proposed model EWT-PSO-SA-SVR to predict the container throughput of Dalian Port and Shenzhen Port. In this paper, the proposed model is also compared with other models, including EWT-CPSO-SVR, EWT-CS-SVR, EWT-PSO-SVR, and EWT-FA-SVR. The results show that the proposed hybird model has better performance than other models in predicting for container throughput data of Dalian Port and Shenzhen Port . URL 查看原文 Degree Grantor 兰州大学 Place of Conferral 兰州 Language 中文 Document Type 学位论文 Identifier http://ir.lzu.edu.cn/handle/262010/225165 Collection 数学与统计学院 Recommended CitationGB/T 7714 徐晓梅. 基于混合模型EWT-PSO-SA-SVR的港口吞吐量区间预测[D]. 兰州. 兰州大学,2018.
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