Forecast chaotic time series data by DBNs
T Kuremoto, M Obayashi, K Kobayashi… - … Congress on Image …, 2014 - ieeexplore.ieee.org
T Kuremoto, M Obayashi, K Kobayashi, T Hirata, S Mabu
2014 7th International Congress on Image and Signal Processing, 2014•ieeexplore.ieee.orgDeep belief nets (DBNs) with multiple artificial neural networks (ANNs) have attracted many
researchers recently. In this paper, we propose to compose restricted Boltzmann machine
(RBM) and multi-layer perceptron (MLP) as a DBN to predict chaotic time series data, such
as the Lorenz chaos and the Henon map. Experiment results showed that in the sense of
prediction precision, the novel DBN performed better than the conventional DBN with RBMs.
researchers recently. In this paper, we propose to compose restricted Boltzmann machine
(RBM) and multi-layer perceptron (MLP) as a DBN to predict chaotic time series data, such
as the Lorenz chaos and the Henon map. Experiment results showed that in the sense of
prediction precision, the novel DBN performed better than the conventional DBN with RBMs.
Deep belief nets (DBNs) with multiple artificial neural networks (ANNs) have attracted many researchers recently. In this paper, we propose to compose restricted Boltzmann machine (RBM) and multi-layer perceptron (MLP) as a DBN to predict chaotic time series data, such as the Lorenz chaos and the Henon map. Experiment results showed that in the sense of prediction precision, the novel DBN performed better than the conventional DBN with RBMs.
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