Research and chemical application of data attributes decomposition based hierarchical ELM neural network

GAO Huihui; HE Yanlin; PENG Di; ZHU Qunxiong

doi:10.3969/j.issn.0438-1157.2013.12.012

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Research and chemical application of data attributes decomposition based hierarchical ELM neural network

更新时间：2025-12-10

- Research and chemical application of data attributes decomposition based hierarchical ELM neural network
- CIESC Journal Issue 12, Pages: 4348-4353(2013)
- 作者机构：
  
  北京化工大学信息科学与技术学院,北京,100029
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2013.12.012
  CLC： P29
- Online First：05 December 2013，
  
  Published：05 December 2013
- 稿件说明：
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GAO Huihui, HE Yanlin, PENG Di, et al. Research and chemical application of data attributes decomposition based hierarchical ELM neural network[J]. CIESC Journal, 2013, (12): 4348-4353.
DOI：

GAO Huihui, HE Yanlin, PENG Di, et al. Research and chemical application of data attributes decomposition based hierarchical ELM neural network[J]. CIESC Journal, 2013, (12): 4348-4353. DOI： 10.3969/j.issn.0438-1157.2013.12.012.

摘要

针对极限学习机（ELM）不能有效处理化工过程中强耦合、带噪声的高维数据建模问题，提出了一种基于数据属性划分的递阶ELM神经网络DHELM。该神经网络采用数据属性划分（DAD）方法对高维输入进行聚类、建立自联想子网，并将自联想子网所提取的特征分量作为极限学习机的输入进行建模。同时，利用UCI标准数据集进行了测试，通过工业应用实例进行了验证，并进行了模型对比。结果表明，DHELM网络在处理复杂高维数据时具有收敛速度快、建模精度高、网络稳定性强的特点，为神经网络发展及其化工应用提供了新思路。

Abstract

The extreme learning machine (ELM) is inefficient in high-dimensional data modeling during the chemical process

where the data is always strongly coupled and with much noise.Aiming at dealing with this problem

a data attributes decomposition based hierarchical ELM neural network (DHELM) is proposed.In the modeling process of DHELM

the data attributes decomposition (DAD) method is used to cluster the high-dimensional inputs and build the auto-associative subnets

and then the extracted characteristic components yielded by auto-associative subnets are inputted to extreme learning machine.Meanwhile

the effectiveness of DHELM is verified by the UCI standard data sets and an industry application object.Through the verification and comparison

the proposed DHELM model has the advantages of fast convergence speed with high modeling accuracy and strong network stability. Furthermore

it provides a new way for neural network development and its application to chemical processes.

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