Liu, Zongying and Loo, Chu Kiong and Masuyama, Naoki and Pasupa, Kitsuchart (2018) Recurrent Kernel Extreme Reservoir Machine for Time Series Prediction. IEEE Access, 6. pp. 19583-19596. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2018.2823336.
Full text not available from this repository.Abstract
This paper proposes a novel recurrent multi-step-ahead prediction model called recurrent kernel extreme reservoir machine (RKERM) with quantum particle swarm optimization (QPSO). This model combines the strengths of recurrent kernel extreme learning machine (RKELM) and modified reservoir computing to overcome the limitations of prediction horizon with increased prediction accuracy based on reservoir computing theory. Furthermore, QPSO is used to optimize the parameters of kernel method and leaking rate of reservoir computing in the RKERM. In the experiment, we apply two synthetic benchmark data sets and five real-world time series data sets, including Malaysia palm oil price, ozone concentration in Toronto, sunspots, Standard Poor's 500, and water level at Phra Chulachomklao Fort in Thailand to evaluate the echo state network, recurrent support vector regression, recurrent extreme learning machine, RKELM, and RKERM. The experimental results show that the RKERM with QPSO has superior abilities in the different predicting horizons than others.
| Item Type: | Article |
|---|---|
| Funders: | Twin Industrial Park under Project RP025B-15HNE, Thailand Research Fund under Grant TRG5680090 |
| Uncontrolled Keywords: | Extreme learning machine; Kernel method; Recurrent neural network; Reservoir computing; Time series prediction |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 May 2019 03:40 |
| Last Modified: | 30 May 2019 03:40 |
| URI: | http://eprints.um.edu.my/id/eprint/21416 |
Actions (login required)
 |
View Item |
