|本期目录/Table of Contents|

[1]高世伟.离散化粒子滤波器及其在色谱柱温度控制中的应用[J].工业仪表与自动化装置,2017,(03):54-57.
　GAO Shiwei.Discrete particle filter and its using in chromatographic column temperature control[J].Industrial Instrumentation & Automation,2017,(03):54-57.
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离散化粒子滤波器及其在色谱柱温度控制中的应用

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2017年03期
页码:
54-57
栏目:
出版日期:
2017-06-15

文章信息/Info

Title:
Discrete particle filter and its using in chromatographic column temperature control
文章编号:
1000-0682（2017）03-0000-00
作者:
高世伟
（兰州石化职业技术学院 电子电气工程系，兰州730060）
Author(s):
GAO Shiwei
(Department of Electric & Electronic Engineering, Lanzhou Petrochemical College of Vocational Technology, Lanzhou 730060, China)
关键词:
关键词：粒子滤波算法; 建议分布; 色谱柱温度
Keywords:
particle filter algorithm;  proposal distribution;  chromatographic column temperature
分类号:
TN713;TH833
DOI:
-
文献标志码:
A
摘要:
由于在非线性、非高斯系统中表现出很好的应用效果，粒子滤波用于很多领域。粒子滤波算法的关键是建议分布的选择。该文提出一种改进的粒子滤波算法，可以提高粒子滤波算法的精度。仿真表明，这种粒子滤波技术应用到色谱柱柱温控制中，可以提高色谱柱温度控制精度，对色谱仪分析结果有重要影响。
Abstract:
Particle filter is widely used in many fields because of its good application effect in nonlinear and non-Gauss systems. The key step in the design of a particle filter is to choose the proposal distribution. In this paper, a particle filter algorithm is presented. Compared?to?conventional?particle filter algorithms, the proposed particle filter method can improve the accuracy of the particle filter algorithm.This algorithm can obtain more accuracy estimate with less particles compared with conventional algorithm. The temperature control of chromatographic column holds the important role to the accuracy of the results of the Chromatograph analysis. The?simulation?results show that using the particle filter algorithm can increase the chromatographic column temperature signal control precision.

参考文献/References:

[1] Benassi R,Bect J,Vazquez E.Bayesian Optimization Using Sequential Monte Carlo[M].Volume 7219 of the series Lecture Notes in Computer Science,2012, 339-342. [2] Moral P D, Doucet A, Ajay Jasra. An adaptive sequential Monte Carlo method for approximate Bayesian computation[J]. Statistics and Computing,2012, 22(5): 1009-1020. [3] Gall J, Potthoff J, Schn?rr C, et al. Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications[J].Journal of Mathematical Imaging and Vision, 2007, 28(1): 1-18. [4] 庞增义,李洪盛.气相色谱仪及其应用[M].昆明:云南科技出版社,1989. [5] 高枝荣,王川,张育红,等.在线色谱分析及其在石化中的应用问题探讨[J].化工自动化及仪表, 2009，36(1):71-74. [6] 张传,张静淑.ZSMY-1型在线色谱模拟蒸馏分析仪的研究与开发[J].中国仪器仪表, 2005(9):102-104. [7] 吴振峰,张传.贾存德.基于可编程器件的在线色谱仪表控制系统[J].中国仪器仪表, 2009(2):56-59. [8] 李建坡.便携式气相色谱检测系统的研制[D].吉林:吉林大学, 2005. [9] Arulampalam M,Maskell S,Gordon N,et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J].IEEE Transactions on Signal Processing,2002, 50(2):174-188. [10] Johannes M, Polson N. Particle Filtering[M]. Handbook of Financial Time Series, 2009:1015-1029. [11] Ristic B, Arulampalam S, Gordon N. Beyond the Kalman Filter: Particle Filters for Tracking Applications [M]. Boston, Artech House Publishers, 2004.

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备注/Memo

备注/Memo:
收稿日期：2016-08-24 作者简介：高世伟（1980），男，湖南岳阳人，博士，高级工程师，副教授，从事石油化工先进检测技术及先进控制技术研究开发工作。
更新日期/Last Update: 1900-01-01