Ship flow prediction method based on VMD-FOA-GRNN

Abstract

The invention discloses a ship flow prediction method based on VMD-FOA-GRNN. The method comprises the following steps: 1, preprocessing ship flow data; 2, performing mutation inspection on the preprocessed ship flow data, and selecting non-mutated ship flow data; 3, performing VMD on the ship flow data which are not mutated, generating a series of IMFs with different frequency scales, and obtaining the decomposed ship flow data; 4, constructing a GRNN based on the FOA, and predicting the decomposed ship flow data to obtain a predicted value; and 5, based on the taste concentration judgment function, performing error analysis on the predicted value and the true value to obtain an average absolute percentage, and completing prediction of the ship flow data. According to the method, the problems that an existing prediction method is not high in prediction precision and does not have universal applicability are solved, the prediction precision of the ship flow is improved based on the generalized regression neural network of variational mode decomposition and fruit fly optimization, the problem of universal applicability of time sequence prediction of complex nonlinear time is solved,and the stability is improved.

Description

technical field [0001] The present invention relates to the technical field of time series, in particular to a ship based on Variational Mode Decomposition-Fruit Fly Optimization Algorithm-General Regression Neural Network (VMD-FOA-GRNN) traffic forecasting method. Background technique [0002] The purpose and significance of the research on ship traffic flow forecasting is to broaden the theory in the field of water transport traffic management and provide technical and theoretical support for water transport management departments. [0003] In the previous research on traffic flow forecasting, one is to classify and bring in the method based on the analysis of the influencing factors of ship traffic flow data, but the limitation of this method is that ship traffic flow is a complex nonlinear system, and its influence Macroscopically, factors include the natural environment of the water area, shipping market conditions, world economy and national policies, etc. Microscopic...

AI Technical Summary

Problems solved by technology

This method aims to solve the problem of low prediction accuracy and general applicability of existing prediction methods. Based on variational mode decomposition and fruit fly optimized generalized regression neural network, it can improve the prediction accuracy of ship flow and solve complex nonlinear problems. The problem of general applicability of time series forecasting, improving stability

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