@inproceedings{dbd71ed55fee40bcab425969f9bd293b,
title = "Intelligent Measurement Modeling Using a Novel Multi-nonlinear Mapping Based Extreme Learning Machine Integrated with Partial Least Square Regression",
abstract = "Accurate intelligent measurement modeling plays a key role in complex process industries. However, establishing an accurate and robust measurement model tends to be more and more difficult because of the increasing complexity in terms of nonlinearity and collinearity of data. To solve this problem, a novel multi-nonlinear mapping based extreme learning machine integrated with partial least square regression is proposed in this paper. In the proposed model, two problems of nonlinearity and collinearity are effectively dealt with by using multi-nonlinear mapping and partial least square regression, respectively. For evaluating performance, empirical studies on a commonly used bench mark problem and a real-world application confirm that the presented method can obtain high accuracy and high stability performance for intelligent measurement.",
keywords = "Ensemble, Extreme Learning Machine, Intelligent measurement, Modeling, Partial least squares regression",
author = "Qunxiong Zhu and Xiaohan Zhang and Yuan Xu and Yanlin He",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020 ; Conference date: 20-11-2020 Through 22-11-2020",
year = "2020",
month = nov,
day = "20",
doi = "10.1109/DDCLS49620.2020.9275221",
language = "English",
series = "Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "539--543",
editor = "Mingxuan Sun and Huaguang Zhang",
booktitle = "Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020",
address = "United States",
}