基于最大相关熵准则的多尺度高斯核极端学习机

doi:10.3969/j.issn.1000-1158.2021.05.18

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摘要针对传统的多尺度核极端学习机对噪声敏感且计算量大的问题,提出一种适用于高斯噪声环境的多尺度核极端学习机。首先,利用最大相关熵准则代替多尺度核极端学习机中传统的最小均方差准则构造目标函数;其次,将1种按训练样本数随机生成尺度因子的多尺度化方法应用于高斯核函数;最后引入拉格朗日乘子法对目标函数进行求解,推导出基于最大相关熵准则的多尺度高斯核极端学习机。实验表明,该算法具有更高的学习效率,与传统多尺度核极端学习机相比,在3个UCI基准数据集上预测精度平均提升30.30%,在对水泥熟料f-CaO含量进行预测的应用实验中预测精度提升23.8%。

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	刘兆伦
	武尤
	王卫涛
	张春兰
	刘彬

关键词 ：计量学, 游离氧化钙含量, 极端学习机, 最大相关熵准则, 多尺度高斯核

Abstract：In view of the fact that the traditional multi-scale kernel extreme learning machine is sensitive to noise and has a large amount of computation, a multi-scale kernel extreme learning machine which is suitable for Gaussian noise environment is proposed. Firstly, the maximum correntropy criterion is used to replace the traditional minimum mean square error criterion in the multi-scale kernel extreme learning machine to construct the objective function. Secondly, a multi-scale method for randomly generating the scale factors according to the training samples number is applied to the Gaussian kernel function. Finally, the Lagrange multiplier method is used to solve the objective function, and the multi-scale Gaussian kernel extreme learning machine based on the maximum correntropy criterion is derived. Experiments show that the proposed algorithm has higher learning efficiency. Comparing with the traditional multi-scale kernel extreme learning machine, the prediction accuracy on the three UCI benchmark data sets and the application experiment for predicting the f-CaO content of cement clinker,increased by an average of 30.30% and 23.8% respectively.

Key words： metrology f-CaO content extreme learning machine maximum correntropy criterion multi-scale Gaussian kernel

收稿日期: 2019-05-16 发布日期: 2021-05-24

PACS:	TB99
	TB973

基金资助:国家自然科学基金(51641609)

通讯作者: 武尤(1995-),女,河北邢台人,燕山大学信息科学与工程学院硕士研究生,主要研究方向为预测控制、神经网络、智能优化。Email: 2931846179@qq.com E-mail: liuzhaolun@ysu.edu.cn

作者简介: 刘兆伦(1978-),男,湖南邵东人,燕山大学信息科学与工程学院副教授、硕士研究生导师,主要从事智能算法及故障诊断方面的研究。Email: liuzhaolun@ysu.edu.cn

引用本文:

刘兆伦,武尤,王卫涛,张春兰,刘彬. 基于最大相关熵准则的多尺度高斯核极端学习机[J]. 计量学报, 2021, 42(5): 658-667.
LIU Zhao-lun,WU You,WANG Wei-tao,ZHANG Chun-lan,LIU Bin. Multi-scale Gaussian Kernel Extreme Learning MachineBased on Maximum Correntropy Criterion. Acta Metrologica Sinica, 2021, 42(5): 658-667.

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