Soft Sensor Modeling Based on MLCRBMs-ELM

NI Jian-hua; WANG jing

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Soft Sensor Modeling Based on MLCRBMs-ELM

更新时间：2026-09-04

- Soft Sensor Modeling Based on MLCRBMs-ELM
- Vol. 35, Issue 10, Pages: 7-12(2018)
- 作者机构：
  
  南京工业大学机构计算机科学与技术学院,江苏,南京,211816
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
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倪健华, 王静. 基于MLCRBMs-ELM的软测量建模[J]. 微电子学与计算机, 2018, 35(10): 7-12.

NI Jian-hua, WANG jing. Soft Sensor Modeling Based on MLCRBMs-ELM[J]. 2018, 35(10): 7-12.
倪健华, 王静. 基于MLCRBMs-ELM的软测量建模[J]. 微电子学与计算机, 2018, 35(10): 7-12. DOI：

NI Jian-hua, WANG jing. Soft Sensor Modeling Based on MLCRBMs-ELM[J]. 2018, 35(10): 7-12. DOI：

摘要

针对辅助变量之间相关性差挖掘困难等问题

提出一种基于特征提取集和极限学习机(MLCRBM-ELML)的软测量建模方法.该方法首先通过聚类算法对输入数据集进行属性簇划分.按结果将数据集分类后输入至MLCRBM特征提取集中进行同步的特征提取

提取到的特征子集经Blending层进行非线性融合得到新特征集

并输入至极限学习机(ELM)进行拟合得到最后的估计结果.实验结果表明该方法优于传统的方法

具有更高的预测精度和泛化性能.

Abstract

Aiming at the difficult problem of poor correlation between auxiliary variables

this paper proposes a soft sensor modeling method based on feature extraction sets and extreme learning machine (MLCRBMs-ELML). First of all

it divides the attribute of input into several classes by clustering algorithm. The classed data set enter to the MLCRBM feature extraction to perform synchronous feature extraction. Then

the extracted feature subsets are nonlinearly merged by the Blending layer and obtain the new feature set. Finally

the new feature set enter the ELM model to get the fitting results. The experimental results show that the soft sensor model is superior to the traditional method

and has higher prediction accuracy and generalization performance.

关键词

Keywords

references

Ackley D H, Hinton G E, Sejnowski T J. A learning algorithm for Boltzmann machines[M] Ablex Publishing Corp. 1985:147-169. DOI: 147

Melchior J, Fischer A, Wiskott L. How to center deep boltzmann machines[J]. Journal of Machine Learning Research, 2016, 17(99):61. DOI: 61

Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: a new learning scheme of feedforward neural networks[C]// IEEE International Joint Conference on Neural Networks. IEEE, 2005: 985-990.

Guo L, Hao J, Liu M. An incremental extreme learning machine for online sequential learning problems [J]. Neurocomputing, 2014, 128(27):50-58. DOI: 50

曹鹏飞, 罗雄麟.化工过程软测量建模方法研究进展[J].化工学报, 2013, 64(3):788-800. DOI: 788

Min H, Luo X L. Calibration of soft sensor by using Just-in-time modeling and AdaBoost learning method[J]. Chinese Journal of Chemical Engineering, 2016, 24(8):1038-1046. DOI: 1038

汤健, 赵立杰, 岳恒, 等.湿式球磨机筒体振动信号分析及负荷软测量[J].东北大学学报(自然科学版), 2010, 31(11):1521-1524. DOI: 1521

王宇红, 狄克松, 张姗, 等.基于DBN-ELM的聚丙烯熔融指数的软测量[J].化工学报, 2016, 67(12):5163-5168. DOI: 5163

张振, 徐立中, 韩华, 等.基于径向基神经网络的明渠流量软测量方法[J].仪器仪表学报, 2011, 32(12):2648-2655. DOI: 2648

常鹏, 王普, 高学金, 等.基于统计量模式分析的MKPLS间歇过程监控与质量预报[J].仪器仪表学报, 2014, 35(6):1409-1416. DOI: 1409

Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1/3):489-501. DOI: 489

Saul L K, Roweis S T. Think globally, fit locally: unsupervised learning of low dimensional manifolds[J]. Journal of Machine Learning Research, 2003, 4(2):119-155. DOI: 119

Ackley D H, Hinton G E, Sejnowski T J. A learning algorithm for boltzmann machines [J]. Cognitive Science, 1985, 9(1):147-169. DOI: 147

严东, 汤健, 赵立杰.基于特征提取和极限学习机的软测量方法[J].控制工程, 2013, 20(1):55-58. DOI: 55

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