Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A ship motion prediction method based on time-frequency analysis and bp neural network

A BP neural network, ship motion technology, applied in the field of ship motion prediction, can solve the problems of nonlinear and randomness of motion, which make research difficult, and achieve the effect of shortening preparation time, high resolution, and accurate fitting model

Active Publication Date: 2016-05-25
BEIHANG UNIV
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In practice, it is of great value to predict the movement of ships, but the nonlinearity and randomness of the movement bring difficulties to the research

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
  • A ship motion prediction method based on time-frequency analysis and bp neural network
  • A ship motion prediction method based on time-frequency analysis and bp neural network
  • A ship motion prediction method based on time-frequency analysis and bp neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0028] Such as figure 1 Shown, the present invention is a kind of ship motion prediction method based on time-frequency analysis and BP neural network, and its steps are as follows:

[0029] (1) Use the ship motion simulator to obtain the simulation data of the ship motion. The sampling frequency of the simulation data is 50 Hz, and a total of 50 s of data is collected. figure 2 Obtain simulated data of the ship's motion for the simulator. Depend on figure 2 It can be seen that the ship motion data contains periodic motion and noise of multiple frequencies. The nonlinear model of ship motion is constructed as follows:

[0030] Y=A 1 ×sin(ω 1 ×t+B 1 )+A 2 ×sin(ω 2 ×t+B 2 )+W(1)

[0031] Among them, Y is the amplitude of the ship motion, A 1 is the amplitude of the main periodic motion, ω 1 is the frequency of the main periodic motion, B 1 is the phase of the main periodic motion, A 2 is the magnitude of the secondary periodic motion, ω 2 is the frequency of the...

Embodiment 2

[0044] The present invention is used to carry out ship motion prediction experiments on 5 groups of ship motion simulation data. Table 2 is the standard deviation of ship motion forecast. It can be seen that the present invention has better prediction accuracy of ship motion under different ship motion conditions.

[0045] Table 2 is the experimental verification effect (m) of the present invention

[0046]

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 motion prediction method based on time-frequency analysis and a BP neural network. The ship motion prediction method includes the first step of carrying out data sampling on ship motion by using a ship motion sensor, the second step of using a Marple method in autoregressive (AR) spectral analysis to obtain main periodic motion component of ship motion, the third step of using a wavelet analysis method in time-domain analysis to carry out analysis and pre-processing on original data of ship motion and decomposing the original data to obtain a trend term and a noise term of ship motion, and the fourth step of using the BP neural network to fit the trend term to obtain a nonlinear motion model of a ship through fitting, and accordingly providing short-term ship motion prediction. The ship motion prediction method completely meets the requirements of engineering application for real-time performance, and has important significance in studying ship motion predication methods under different sea conditions.

Description

technical field [0001] The invention relates to a ship motion prediction method, which is suitable for short-term motion prediction and system application on ships, and in particular to a ship motion prediction method based on time-frequency analysis and BP neural network. Background technique [0002] The purpose of this subject research is to explore a new forecast method to describe the ship attitude motion under high sea conditions, and provide technical support for the real-time adaptive control of the ship and its equipment motion attitude by analyzing its motion law. [0003] When a ship is sailing at sea, it will produce six degrees of freedom swaying motion when disturbed by waves. Due to the complex and changeable conditions of the actual sea waves, the motion of the ship is also very complicated, and the motions of each degree of freedom are coupled with each other to form a complex nonlinear system. In practice, it is of great value to predict the motion of ship...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00G06N3/08
Inventor 王玮丁振兴孟跃王蕾张谦
Owner BEIHANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com