Anti-interference model of intelligent control for high-precision regulation of pressure in industrial low pressure area based on neural network
Year: 2025
Type: conference-paper
Abstract: As science and technology progress, the traditional pressure regulation system is being phased out, and researchers today are focusing on the study of higher precision regulation systems as a crucial task. However, there are still problems with pressure regulation in low pressure areas. In this regard, a high-precision regulation model for low-pressure zone pressure based on neural networks is proposed, which applies the neural system to pressure regulation in low-pressure zones to achieve the purpose of high-precision regulation. Neural network is a computing model that imitates the structure and function of the human brain. It can achieve complex nonlinear mapping and intelligent processing by learning large amounts of data. Applying the nervous system to the field of pressure regulation can adaptively learn the load characteristics of the pressure system, thereby improving the accuracy and stability of load prediction. Real-time monitoring and fault diagnosis can also be performed on the operating status of the pressure system, thereby improving the reliability and safety of the system. And optimize and adjust the control strategy of the pressure system, so as to improve the efficiency and energy saving of the system. Based on this, this paper proposes to combine BP algorithm and particle swarm optimization algorithm to obtain PSOB model. stability. And it can adaptively adjust network parameters and control strategies, adapt to different working conditions and environmental disturbances, and improve control flexibility and intelligence. Historical data and real-time data can also be used for learning and updating to improve control accuracy and prediction capabilities.
DOI: 10.1117/12.3062809
Authors: Yan Ren, Yunjiang Wang
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409784704