An Improved Recurrent Neural Network for Industrial Control System Identification

Konferenz: ECITech 2022 - The 2022 International Conference on Electrical, Control and Information Technology
25.03.2022 - 27.03.2022 in Kunming, China

Tagungsband: ECITech 2022

Seiten: 4Sprache: EnglischTyp: PDF

Autoren:
Sun, Yu; Tian, Bin (Beijing Engineering Research Center of Power Station Automation, CHN Energy Zhishen Control Technology Co., Ltd Beijing, China)
Qiu, Shirong; Ji, Lianen (Department of Computer Science and Technology China University of Petroleum Beijing, China)

Inhalt:
The rapid development of industrial automation makes the control process more and more demanding for the accuracy of system identification. Finding an effective identification model for regulating and controlling the time-critical and nonlinear process remains a difficult problem. Therefore, this paper proposes an improved recurrent neural network for industrial control system identification, called SI-RNNs, to solve the problem of insufficient prediction accuracy of the traditional RNNs in the initial time steps. SI-RNNs adds the state initialization process to determine the initial operational state of the control systems and selects the optimal state initialization steps length by perturbating feature values approach. In practical cases, it is verified that our method can effectively improve the solution accuracy of the model for industrial control process, especially in the gated recurrent neural networks capable of long-term memory.