System Change Detection Method Using Recurrent Neural Networks

被引:1
|
作者
Hayashida, Tomohiro [1 ]
Yamamoto, Toru [2 ,3 ]
Kinoshita, Takuya [1 ]
Nishizaki, Ichiro [4 ,5 ]
Sekizaki, Shinya [3 ]
Hiratsuka, Naoto [1 ]
机构
[1] Hiroshima Univ, Higashihiroshima, Hiroshima, Japan
[2] Hiroshima Univ, Div Engn & Informat Pedag, Grad Sch Pedag, Higashihiroshima, Hiroshima, Japan
[3] Hiroshima Univ, Div Math, Dept Elect & Elect Syst, Grad Sch Engn, Higashihiroshima, Hiroshima, Japan
[4] Hiroshima Univ, Sch Engn, Higashihiroshima, Hiroshima, Japan
[5] Hiroshima Univ, Grad Sch Engn, Higashihiroshima, Hiroshima, Japan
关键词
time-series forecasting; recurrent neural networks; failure detection;
D O I
10.1002/ecj.12020
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A single or multiple kinds of internal or external environmental variations of the system often cause the property variation of any system under control, and the readjustment of controller parameters is required. To maintain high performance of controlling and minimize the total cost for readjustments of the controller parameters, determination of the appropriate timing for readjustment of the controller parameters is important. This paper proposes new procedure to determine the appropriate timing for the readjustments based on the time-series data using the recurrent neural networks (RNNs). A well-coordinated RNN with proper structure has high performance on the time-series data forecasting with the assistance of its internal signal feedback structure. This paper conducts some numerical experiments to verify the availability of the proposed method to some systems. The experimental result indicates that the proposed method has higher performance than other existing method with the same aim.
引用
收藏
页码:39 / 46
页数:8
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