Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Ship flow prediction method based on deep learning

A technology of ship flow and prediction method, which is applied in the field of ship flow prediction of IF-CEEMD-LSTM, which can solve the problems of low time series prediction accuracy, disappearance of the gradient of the recurrent neural network, and the model cannot meet the prediction accuracy.

Pending Publication Date: 2020-06-05
SHANGHAI MARITIME UNIVERSITY
View PDF7 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Since a single model cannot satisfy the forecasting accuracy, current time series forecasting methods are an effective combination of two or more models
[0005] In view of the low precision of the existing time series forecasting technology, the characteristics of nonlinearity and non-stationarity of ship flow data, and the disappearance of gradients in the cyclic neural network, it is necessary to develop an improved IF-CEEMD-LSTM (IF refers to Isolation Forest, that is, Isolation forest algorithm; CEEMD refers to the improved complementary set empirical mode decomposition; LSTM refers to the Long Short Term network (Long Short Term Memory Neural Network) prediction method, which has good adaptability to long-term or short-term time series data prediction. Superior performance in prediction

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Ship flow prediction method based on deep learning
  • Ship flow prediction method based on deep learning
  • Ship flow prediction method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0068] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0069] The present invention provides a ship traffic forecasting method based on the improved IF-CEEMD-LSTM (isolated forest-complementary ensemble empirical mode decomposition-long short-term memory neural network), first removes the abnormal components in the original ship data with the forest isolation algorithm Post-normal...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a ship flow prediction method based on deep learning. The ship flow prediction method is based on an improved isolated forest-complementary set empirical mode decomposition-long short-term memory neural network. Firstly, the problems of noise and abnormal points in original data are considered, and the abnormal points in the data are eliminated by using an isolated forest algorithm; secondly, in order to further improve the prediction precision, input data is decomposed into intrinsic mode function components and residual components of different frequencies by using animproved complementary set empirical mode decomposition algorithm, each intrinsic mode function and residual error are predicted by using a long-term and short-term memory neural network separately, and finally, superposition reconstruction is carried out on a prediction result. According to the method, the prediction precision is improved, and the method has good adaptability to long-term or short-term time series data.

Description

technical field [0001] The present invention relates to the technical field of time series forecasting, in particular to a deep learning-based ship flow forecasting method, in particular to an improved IF-CEEMD-LSTM (isolated forest-complementary set empirical mode decomposition-long short-term memory neural network ) ship flow forecasting method. Background technique [0002] With the increase of modern maritime trade, the prediction accuracy of maritime ship flow is necessary to ensure the efficiency and safety of ship traffic, to broaden the theory of water transportation, and to port and maritime departments for port construction and development and effective use of waterways. However, the traffic flow forecasting process is more complicated and affected by many factors, such as season, GDP, port container throughput, port cargo throughput, etc. [0003] In the past, most of the traditional machine learning methods were used to model and predict ship flow. According to ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06F17/14G06Q10/04G06Q10/08G06Q50/30G06N3/04G06N3/08
CPCG06F17/14G06Q10/04G06Q10/083G06N3/08G06N3/044G06N3/045G06Q50/40
Inventor 武绘芹黄洪琼
Owner SHANGHAI MARITIME UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products