Electrical Engineering Design Method Based on Neural Network and Application of Automatic Control System
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초록
The existing electrical engineering design method and the dynamic objective function in the application processof automatic control system fail to meet the unbounded condition, which affects the control tracking accuracy. In order to improve the tracking control accuracy, this paper studies the electrical engineering design methodbased on neural network and the application of automatic control system. This paper analyzes the structure andworking mechanism of electrical engineering automation control system by an automation control model withmain control objectives. Following the analysis, an optimal solution of controllability design and fault-tolerantcontrol is figured out. The automatic control power coefficient is distributed based on an ideal control effect ofsystem. According to the distribution results, an automatic control algorithm is based on neural network foraccurate control. The experimental results show that the electrical automation control method based on neuralnetwork can significantly reduce the control following error to 3.62%, improve the accuracy of the electricalautomation tracking control, thus meeting the actual production needs of electrical engineering automationcontrol system.
Abstract
The existing electrical engineering design method and the dynamic objective function in the application processof automatic control system fail to meet the unbounded condition, which affects the control tracking accuracy. In order to improve the tracking control accuracy, this paper studies the electrical engineering design methodbased on neural network and the application of automatic control system. This paper analyzes the structure andworking mechanism of electrical engineering automation control system by an automation control model withmain control objectives. Following the analysis, an optimal solution of controllability design and fault-tolerantcontrol is figured out. The automatic control power coefficient is distributed based on an ideal control effect ofsystem. According to the distribution results, an automatic control algorithm is based on neural network foraccurate control. The experimental results show that the electrical automation control method based on neuralnetwork can significantly reduce the control following error to 3.62%, improve the accuracy of the electricalautomation tracking control, thus meeting the actual production needs of electrical engineering automationcontrol system.
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