It is one of the responsibilities of the navigation support department to ensure the correct layout position of the light buoy and provide as accurate position information as possible for ship navigation and positioning.
If the position deviation of the light buoy is too large to be detected in time, sending wrong navigation assistance
information to the ship will directly affect the navigation safety of the ship and increase the pressure on the
management department. Therefore, mastering the offset characteristics of light buoy is of great significance for
the maintenance of light buoy and improving the navigation aid efficiency of light buoy. Kernel density estimation
can intuitively express the spatial and temporal distribution characteristics of buoy position, and indicates the
intensive areas of buoy position in the channel. In this paper, in order to speed up deciding the optimal variable
width of kernel density estimator, an improved adaptive variable width kernel density estimator is proposed, which
reduces the risk of too smooth probability density estimation phenomenon and improves the estimation accuracy
of probability density. A fractional recurrent neural network is designed to search the optimal bandwidth of
kernel density estimator. It not only achieves faster training speed, but also improves the estimation accuracy of
probability density.
徐良坤1
,
2，薛晗2，金永兴1，周世波2
. Neural Network Optimization of Multivariate KDE Bandwidth for
Buoy Spatial Information[J]. Journal of Shanghai Jiaotong University(Science), 2024
, 29(5)
: 773
-779
.
DOI: 10.1007/s12204-022-2466-x
[1] XIA Y D, DING Q, JING N J, et al. An enhanced fault detection method for centrifugal chillers using kernel density estimation based kernel entropy component analysis [J]. International Journal of Refrigeration, 2021, 129: 290-300.
[2] DAI J H, LIU Y, CHEN J L. Feature selection via max-independent ratio and min-redundant ratio based on adaptive weighted kernel density estimation [J]. Information Sciences, 2021, 568: 86-112.
[3] HE Y Y, FAN H L, LEI X H, et al. A runoff probability density prediction method based on B-spline quantile regression and kernel density estimation [J]. Applied Mathematical Modelling, 2021, 93: 852-867.
[4] HUANG L, LIU Y, WEN Y Q, et al. Inland waterway sparse AIS trajectory estimation method based on navigation experience [J]. Journal of Dalian Maritime University, 2017, 43(3): 7-13 (in Chinese).
[5] LIU H, LI W F. Automatic recognition of marine traffic flow with AIS data [J]. Navigation of China, 2016, 39(4): 87-90 (in Chinese).
[6] WU T J, CHEN C F, CHEN H Y. A variable bandwidth selector in multivariate kernel density estimation [J]. Statistics & Probability Letters, 2007, 77(4):462-467.
[7] KERM P. Adaptive kernel density estimation [J]. The Stata Journal: Promoting Communications on Statistics and Stata, 2003, 3(2): 148-156.
[8] XU Z Q, XU C, HU J, et al. Robust resistance to noise and outliers: Screened Poisson Surface Reconstruction using adaptive kernel density estimation [J]. Computers & Graphics, 2021, 97: 19-27.
[9] LI Z W, HE P. Data-based optimal bandwidth for kernel density estimation of statistical samples [J]. Communications in Theoretical Physics, 2018, 70(6): 728.
[10] EL HEDA K, LOUANI D. Optimal bandwidth selection in kernel density estimation for continuous time dependent processes [J]. Statistics & Probability Letters, 2018, 138: 9-19.
[11] KRAML J, HOFER F, QUOIKA P K, et al. Xentropy: A parallelized kernel density estimator with automated bandwidth selection to calculate entropy [J]. Journal of Chemical Information and Modeling,2021, 61(4): 1533-1538.
[12] JIN H S, HE Y L, CHANG X Y, et al. An improved kernel density estimator with adaptive variable bandwidth [J]. Journal of Shenzhen University (Science and Engineering), 2019, 36(6): 709-717 (in Chinese).
[13] JING X G, GE L G, SUN W. A clustering algorithm for sea buoy trajectory based on Gaussian mixture model [J]. Modern Computer, 2017(36): 3-5, 8 (in Chinese).
[14] LU J. A buoy position correction algorithm [J]. Ship Electronic Engineering, 2021(5): 143-145 (in Chinese).
[15] XU J L, GAO S, GE Y, et al. Analysis of wave effect on drifting trajectory of float at sea [J]. Journal of Institute of Disaster Prevention, 2017, 19(2): 75-79 (in Chinese).
[16] SILVERMAN B W. Density estimation for statistics and data analysis [M]. New York: Chapman and Hall/CRC, 1986.
[17] RU Y. Algorithm of kernel density estimation of kernel function [D]. Harbin: Harbin University of Science and Technology, 2016 (in Chinese).
