| Title: | OS12-3 Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning |
|---|---|
| Publication: | ICAROB2017 |
| Volume: | 22 |
| Pages: | 658-661 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2017.OS12-3 |
| Author(s): | Takaomi Hirata, Takashi Kuremoto, Masanao Obayashi, Shingo Mabu, Kunikazu Kobayashi |
| Publication Date: | January 19, 2017 |
| Keywords: | deep learning, restricted Boltzmann machine, stochastic gradient ascent, reinforcement learning, errorbackpropagation |
| Abstract: | Hinton's deep auto-encoder (DAE) with multiple restricted Boltzmann machines (RBMs) is trained by the unsupervised learning of RBMs and fine-tuned by the supervised learning with error-backpropagation (BP). Kuremoto et al. proposed a deep belief network (DBN) with RBMs as a time series predictor, and used the same training methods as DAE. Recently, Hirata et al. proposed to fine-tune the DBN with a reinforcement learning (RL) algorithm named "Stochastic Gradient Ascent (SGA)" proposed by Kimura & Kobayashi and showed the priority to the conventional training method by a benchmark time series data CATS. In this paper, DBN with SGA is invested its effectiveness for real time series data. Experiments using atmospheric CO2 concentration, sunspot number, and Darwin sea level pressures were reported. |
| PDF File: | https://alife-robotics.co.jp/members2017/icarob/data/html/data/OS_pdf/OS12/OS12-3.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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