Multi-manifold soft sensor based on modified expanding search clustering algorithm

JI Wenpeng; YANG Huizhong

doi:10.11949/j.issn.0438-1157.20181364

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Multi-manifold soft sensor based on modified expanding search clustering algorithm

Process system engineering | 更新时间：2026-07-24

- Multi-manifold soft sensor based on modified expanding search clustering algorithm
- CIESC Journal Vol. 70, Issue 2, Pages: 723-729(2019)
- 作者机构：
  
  江南大学轻工过程先进控制教育部重点实验室，江苏无锡 214122
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20181364
  CLC： TP 274
- Received：18 November 2018，
  
  Revised：2018-11-22，
  
  Online First：16 June 2020，
  
  Published：05 February 2019
- 稿件说明：
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吉文鹏, 杨慧中. 基于改进扩张搜索聚类算法的多流形软测量建模[J]. 化工学报, 2019, 70(2): 723-729

JI Wenpeng, YANG Huizhong. Multi-manifold soft sensor based on modified expanding search clustering algorithm[J]. CIESC Journal, 2019, 70(2): 723-729
吉文鹏, 杨慧中. 基于改进扩张搜索聚类算法的多流形软测量建模[J]. 化工学报, 2019, 70(2): 723-729 DOI： 10.11949/j.issn.0438-1157.20181364.

JI Wenpeng, YANG Huizhong. Multi-manifold soft sensor based on modified expanding search clustering algorithm[J]. CIESC Journal, 2019, 70(2): 723-729 DOI： 10.11949/j.issn.0438-1157.20181364.

摘要

针对化工生产过程工况复杂多变，单一的软测量模型难以满足系统对估计精度的要求，提出了一种基于改进的扩张搜索聚类算法的多流形软测量建模的方法。该算法采用流形距离来代替欧氏距离，自适应地确定邻域半径，并引入局部密度用于确定聚类中心，对聚类后得到的各个子流形分别采用流形学习中的核等距映射法进行特征提取，建立基于高斯过程回归的子模型。将该方法应用于某双酚A生产装置的软测量建模，仿真结果验证了该方法的有效性。

Abstract

Due to the complex and changeable working conditions in chemical production process

a single soft sensor model cannot meet the requirements of the system for estimation accuracy. A new method of multi-manifold modeling is proposed in this paper based on a modified expanding search clustering algorithm. This algorithm uses the distance between manifolds instead of the Euclidean distance

adaptively determines the neighborhood radius

and introduces the local density to determine the center of clustering. The features of sub-manifold obtained after clustering are extracted by kernel isometric mapping method in manifold leaning respectively

and develop sub-models based on Gaussian process regression. The method was applied to the soft measurement modeling of a bisphenol A production device. The simulation results verify the effectiveness of the method.

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references

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