Genetic algorithm-based support vector regression shipping traffic flow prediction method

A technology of support vector regression and genetic algorithm, applied in the field of ship traffic flow prediction, can solve the problem of low prediction accuracy of the prediction model, achieve the effect of improving the accuracy and generalization ability, avoiding blindness and high stability

Inactive Publication Date: 2011-04-06
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

[0007] The present invention aims at the problem that the prediction accuracy of the existing ship traffic flow prediction model is not high, and provides a

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  • Genetic algorithm-based support vector regression shipping traffic flow prediction method
  • Genetic algorithm-based support vector regression shipping traffic flow prediction method
  • Genetic algorithm-based support vector regression shipping traffic flow prediction method

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[0032] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific illustrations.

[0033] The present invention proposes the ship flow prediction method of the improved support vector machine at the deficiencies of the existing ship flow prediction method, and its steps are as follows (see figure 1 ):

[0034] Step 1: Reduce the dimensionality of factors that may have an impact on ship flow through weighted principal component analysis, and select factors with a higher cumulative contribution rate.

[0035] The following is a detailed introduction to the general steps of the weighted principal component weighting method:

[0036] (1) collect p-dimensional random vector X=[X 1 , X 2 ,...,X p ] T n samples of x i =[x i1 , x i2 ,...,x ip ] T , sort out the observation data matrix X=(x ij ) n×p ;

[0037] (2) Determin...

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Abstract

The invention discloses a genetic algorithm-based support vector regression shipping traffic flow prediction method, comprising the following steps: (1) reducing the dimensional number of factors possibly generating influences on shipping traffic flow by a weighted principal component analysis method, and selecting influencing factors with higher cumulative contribution rate; (2) carrying out normalization preprocessing on original vessel traffic flow time series data to generate a data set and then grouping; (3) selecting a kernel function to determine support vector machine (SVM) regression parameters; (4) constructing a support vector regression prediction model optimized by a genetic algorithm; (5) inputting the data set to generate a prediction function; and (6) predicting according to the prediction function generated in step (5), evaluating and analyzing prediction error, and if the error is relatively large, returning to step (2) and regulating the parameters again, predicting once again. The method of the invention has the advantages of higher prediction accuracy and higher stability of prediction accuracy.

Description

technical field [0001] The invention relates to a ship traffic flow prediction technology, in particular to a support vector regression ship traffic flow prediction method based on genetic algorithm optimization. Background technique [0002] The forecast research of ship traffic flow is inseparable from the establishment of ship routing system. The formulation of the ship routing system requires a clear understanding of the recent and future overall ship traffic flow in the water area or waterway. The flow forecast provides basic flow data for the planning, design and optimization of future waterway routes, which is also the basis for formulating management policies and programs. The most basic and important basis. [0003] The CSFM model proposed by Lu Jing and Fang Xianglin of Dalian Maritime University is based on the analysis of ship traffic flow in the waters of the main coastal ports in my country, and considering the shortcomings of the past prediction methods. The...

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Application Information

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IPC IPC(8): G08G3/00
Inventor 张浩肖英杰白响恩杨小军李松郑剑
Owner SHANGHAI MARITIME UNIVERSITY
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