Abstract
By dealing with robust modeling and online learning together in a unified random vector functional-link networks (RVFLNs) framework, this paper presents a novel robust online sequential RVFLNs for data modeling of dynamic time-varying systems together with its application for a blast furnace (BF) ironmaking process. First, to overcome the difficulties caused by the nonlinear time-varying dynamics of process and to enable the RVFLNs to learn online and to avoid data saturation, an improved online sequential version of RVFLNs (OS-RVFLNs) is presented by sequential learning with forgetting factor. It has been shown that the improved OS-RVFLNs with forgetting factor is not only suitable for the large-scale and real-time data transfer situation but also can adjust the sensitivity of the algorithm to different samples. Second, in order to solve the issue of modeling robustness when the dataset is contaminated with various outliers, a Cauchy distribution function weighted M-estimator is introduced to strengthen the robustness of the improved OS-RVFLNs. The non-Gaussian Cauchy distribution function is used to estimate the weights of different data and thus the corresponding contribution on modeling can be properly distinguished. Experiments using actual industrial data of a large BF ironmaking process have demonstrated that the proposed algorithm produces a much stronger robustness and better estimation accuracy than other algorithms.
| Original language | English |
|---|---|
| Article number | 8740951 |
| Pages (from-to) | 4783-4795 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Cybernetics |
| Volume | 50 |
| Issue number | 11 |
| DOIs | |
| State | Published - Nov 2020 |
| Externally published | Yes |
Funding
Manuscript received July 5, 2018; revised December 16, 2018 and March 26, 2019; accepted May 27, 2019. Date of publication June 19, 2019; date of current version October 26, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61890934, Grant 61473064, Grant 61790572, and Grant 61290323, and in part by the Research Funds for the Central Universities under Grant N180802003. This paper was recommended by Associate Editor H. Gao. (Corresponding author: Ping Zhou.) P. Zhou, W. Li, M. Li, and T. Chai are with the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China (e-mail: [email protected]). This work was supported in part by the National Natural Science Foundation of China under Grant 61890934, Grant 61473064, Grant 61790572, and Grant 61290323, and in part by the Research Funds for the Central Universities under Grant N180802003.
| Funders | Funder number |
|---|---|
| Research Funds for the Central Universities | |
| National Natural Science Foundation of China | 61473064, 61790572, 61290323, 61890934 |
| Fundamental Research Funds for the Central Universities | N180802003 |
Keywords
- Blast furnace (BF)
- data modeling
- dynamic time-varying system
- online sequential RVFLNs (OS-RVFLNs)
- robust OS-RVFLNs (R-OS-RVFLNs)
- robust modeling