Modified online sequential extreme learning machine algorithm using model predictive control approach
This paper stresses its contribution based on improving the learning dynamics of the online sequential extreme learning machine (OS-ELM) algorithm using a control system approach. We develop a predictive learning framework that enables optimization with a finite horizon using model predictive contro...
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| Veröffentlicht in: | Intelligent systems with applications Jg. 18; S. 200191 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
01.05.2023
Elsevier |
| Schlagworte: | |
| ISSN: | 2667-3053, 2667-3053 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | This paper stresses its contribution based on improving the learning dynamics of the online sequential extreme learning machine (OS-ELM) algorithm using a control system approach. We develop a predictive learning framework that enables optimization with a finite horizon using model predictive control (MPC). A Lyapunov inequality function for a discrete-time linear time-varying systems (DLTV) systems is utilized to guarantees learning dynamics stability. The numerical finding shows that the learning dynamics of our approach fit the sequential learning in the OS-ELM. To enhance the performance, we combine our model with principal component analysis (PCA) for dimensionality reduction and robust principal component analysis (RPCA) for handling data outliers. In this paper, two models were proposed: Alg. 1 is a modified OS-ELM with PCA, and Alg. 2 is a modified OS-ELM with RPCA. The experiment on regression and classification tasks has been conducted to show the efficacy of our proposed models. For regression tasks, our proposed model shows significant results in reducing the normalized mean square error (nRMSE). For the classification tasks, the accuracy performance has significantly increased. The increasing of the percentage performance improvement rate (PIR%) compared to the classic OS-ELM is reported as follows: Alg. 1 (4.83%) and Alg. 2 (3.03%) for binary classification; Alg. 1 (8.54%) and Alg. 2 (7.54%). The region of curve-area under curve (ROC-AUC) provides better discrimination results in differentiating between classes. From evaluation performance indicators, our proposed models show competitive results compared to other ELM types, such as kernel-based ELM (K-ELM) and multi-layer ELM (ML-OSELM and ML-ELM). We apply our proposed models for human gesture recognition to a case study of traffic gestures used by Indonesian police to regulate traffic flow. The experiment results show significant improvement in classifying human gestures, i.e., weighted-accuracy performance: Alg. 1 (93.8%); Alg. 2 (93.2%); and OS-ELM (81.8%).
•We proposed a novel, modified OS-ELM based on control perspective using a DLTV model and a MPC scheme to improve its learning dynamics.•We investigate the stability condition of our learning model based on the Lyapunov stability theorem for the closed-loop system.•We apply our proposed model to a case study of the Indonesian police's traffic control gestures to determine the command instructions for the traffic flow control on the road with a significant result.•Numerical experiments show significant results compared to the classic OS-ELM, both in binary and multi-class classification cases, and achieve competitive results compared to several ELM types, including kernel-based ELMs (K-ELM with RBF and polynomial kernel) and multi-layer ELMs (ML-OS-ELM and ML-ELM). |
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| ISSN: | 2667-3053 2667-3053 |
| DOI: | 10.1016/j.iswa.2023.200191 |