A Cycle Deep Belief Network Model for Multivariate Time Series Classification

被引：23

作者：

Wang, Shuqin ^{[1
,2
]}

Hua, Gang ^{[1
]}

Hao, Guosheng ^{[2
]}

Xie, Chunli ^{[2
]}

机构：

[1] China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221116, Jiangsu, Peoples R China

[2] Jiangsu Normal Univ, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2017年 / 2017卷

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1155/2017/9549323

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN ED, KNN DTW, and RNN.

引用

页数：7

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