System change detection method using recurrent neural networks

0Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.
Get full text

Abstract

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 the appropriate timing for readjustment 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 predication of time-series data 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.

Cite

CITATION STYLE

APA

Hayashida, T., Yamamoto, T., Kinoshita, T., Nishizaki, I., Sekizaki, S., & Hiratsuka, N. (2017). System change detection method using recurrent neural networks. IEEJ Transactions on Electronics, Information and Systems, 137(2), 242–248. https://doi.org/10.1541/ieejeiss.137.242

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free