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System Change Detection Method Using Recurrent Neural Networks
Author(s) -
HAYASHIDA TOMOHIRO,
YAMAMOTO TORU,
KINOSHITA TAKUYA,
NISHIZAKI ICHIRO,
SEKIZAKI SHINYA,
HIRATSUKA NAOTO
Publication year - 2018
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.12020
Subject(s) - recurrent neural network , computer science , controller (irrigation) , property (philosophy) , control theory (sociology) , signal (programming language) , artificial neural network , series (stratigraphy) , time series , control (management) , control engineering , artificial intelligence , engineering , machine learning , paleontology , philosophy , epistemology , agronomy , biology , programming language
SUMMARY 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.