Forecast Chaotic Time Series Data by DBNs

被引：0

作者：

Kuremoto, Takashi ^{[1
]}

Hirata, Takaomi ^{[1
]}

Obayashi, Masanao ^{[1
]}

Mabu, Shingo ^{[1
]}

Kobayashi, Kunikazu ^{[2
]}

机构：

[1] Yamaguchi Univ, Grad Sch Sci & Engn, Ube, Yamaguchi 755, Japan

[2] Aichi Prefectural Univ, Sch Informat Sci & Technol, Nagakute, Aichi, Japan

来源：

2014 7TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING (CISP 2014) | 2014年

关键词：

Deep Belief Net; Deep learning; forecasting; chaos; LEARNING ALGORITHM;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：1130 / 1135

页数：6

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