A Prediction Method for Blood Glucose Based on Grey Wolf Optimization Evolving Kernel Extreme Learning Machine

被引：0
|
作者：
Chen, Xiaoyu [1 ]
Tuo, Jianyong [1 ]
Wang, Youqing [1 ,2 ]
机构：
[1] Beijing Univ Chem Technol, Beijing 100029, Peoples R China
[2] Shandong Univ Sci & Technol, Qingdao 266590, Peoples R China
来源：
PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC) | 2019年
基金：
中国国家自然科学基金;
关键词：
Type; 1; diabetes; Blood glucose prediction; Kernel extreme learning machine; Grey wolf optimization; REGRESSION;
D O I：
10.23919/chicc.2019.8866210
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Patients with type 1 diabetes need to acquire their blood glucose prediction values which can make sure the actual value is well controlled within the normal range. Therefore, the accuracy of the blood glucose prediction method is very important. In this paper, the radial basis function kernel extreme learning machine (KELM) is used to predict blood glucose, and the parameters are adjusted by the grey wolf optimization (GWO) algorithm. Experiment results show that KELM based on GWO algorithm has great robustness and better generalization performance comparison with traditional extreme learning machine algorithm. The GWO-KELM model achieved high prediction accuracy.
引用
收藏
页码：3000 / 3005
页数：6
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