CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 723-729.doi: 10.11949/j.issn.0438-1157.20181364

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

Wenpeng JI(),Huizhong YANG()   

  1. Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, Jiangsu, China
  • Received:2018-11-18 Revised:2018-11-22 Online:2019-02-05 Published:2018-12-04
  • Contact: Huizhong YANG E-mail:932077192@qq.com;yhz_jn@163.com

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.

Key words: manifold learning, algorithm, model, soft sensor, expanding search clustering, computer simulation

CLC Number: 

  • TP 274

Fig.1

Manifold distance and European distance comparison"

Table 1

Clustering results"

方法子类个数子类分布
MESCA465,68,71,96
ESCA4129,80,49,42

Fig.2

Simulation results"

Table 2

Test errors"

模型本文方法ESCA-GPRKIsomap-GPR
RMSE0.22950.31310.4493
MAXE0.53760.90250.9759
ME0.19160.23120.3742

Fig.3

Absolute error curves"

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