Double-layer compression method based on PLSR-SBR for IP mode measurement and control system

HONG Xiao-bin; LIU Gui-xiong

doi:10.3788/OPE.20101810.2280

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Double-layer compression method based on PLSR-SBR for IP mode measurement and control system

Article | 更新时间：2020-08-12

- Double-layer compression method based on PLSR-SBR for IP mode measurement and control system
- Optics and Precision Engineering Vol. 18, Issue 10, Pages: 2280-2287(2010)
- 作者机构：
  
  华南理工大学机械与汽车工程学院,广东广州 510640
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20101810.2280
  CLC： TN911.7;TP273
- Received：10 December 2009，
  
  Revised：10 March 2010，
  
  Published Online：28 October 2010，
  
  Published：20 October 2010
- 稿件说明：
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洪晓斌, 刘桂雄. 面向IP模式测控系统的PLSR-SBR双层压缩[J]. 光学精密工程, 2010,18(10): 2280-2287

HONG Xiao-bin, LIU Gui-xiong. Double-layer compression method based on PLSR-SBR for IP mode measurement and control system[J]. Editorial Office of Optics and Precision Engineering , 2010,18(10): 2280-2287
洪晓斌, 刘桂雄. 面向IP模式测控系统的PLSR-SBR双层压缩[J]. 光学精密工程, 2010,18(10): 2280-2287 DOI： 10.3788/OPE.20101810.2280.

HONG Xiao-bin, LIU Gui-xiong. Double-layer compression method based on PLSR-SBR for IP mode measurement and control system[J]. Editorial Office of Optics and Precision Engineering , 2010,18(10): 2280-2287 DOI： 10.3788/OPE.20101810.2280.

摘要

针对以太网测控网络存在数据冲突导致系统实时性、可靠性降低问题

提出了基于偏最小二乘回归(PLSR)SBR的双层压缩方法。第一层建立主参量与所有辅助参量的确定模型

利用压缩有效性指标确定主成分

完成主参量的信息压缩。第二层基于改进的SBR

通过选取辅助参量中的基础序列

建立基础信号;在满足拟合误差条件下

逐步将每一个辅助参量序列映射到基础信号上

完成对辅助参量的数据压缩。该方法重点解决辅助参量和主参量中的解释潜变量

和反映潜变量

相关程度最大、基础信号由最少基础序列组成、辅助参量实现最小变长分解个数及基础信号独立更新原则等关键问题。最后将该方法应用于IP模式乙醇浓度测控系统。实验结果表明

在IP模式测控系统同时具有主参量和辅助参量

且不同参量间存在相关性时

该方法可在允许拟合相对误差为5％的情况下

使压缩率达到68％以上

从而有效地降低以太网测控网络数据冲突程度。

Abstract

To address the existing problems in Ethernet measurement and control networks

such as real-time and reliability caused by the collision of measurement and control data

a double-layer compression method based on Partial Least Square Regression-self Based Regression(PLSR-SBR) was induced to the IP Mode Measurement and Control System(IMMCS). In the first layer

the model of main parameters and all auxiliary parameters was built up based on PLSR.The principal score of main parameter was determined by the efficient compression index to finish the information compression of main parameter. In the second layer

the auxiliary parameters were compressed based on a modified SBR to set up a basic signal. To ensure the fitting error to be smaller than the setting value

every auxiliary parameter sequence in the auxiliary parameters or the decomposed sequences were mapped in turn to a definitive basic signal to complete the data compression of the auxiliary parameters. Innovative technologies for some key problems were addressed in the method

including the confirmation of maximum correlation degree of interpreting latent variables and reflecting latent variables

basic signal with least basic sequences

the minimum number of decomposed sequences from auxiliary parameters and the independent update rule of basic signal. Finally

experiments using IMMCS for alcohol concentration were performed. The results show that under the conditions that IMMCS possesses main parameters and auxiliary parameters

and simultaneously data variety of parameters is wide

the compression ratio based on the method reaches 68%

which is higher than that based on the Swinging Door. The data collision of Ethernet measurements and control networks are decreased effectively.

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references

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