Optimization Design of Predictive Response Based on Time Series Forecast Model LSTM in Broadband High Current Feedback Regulation

For the wide-band large current feedback regulation system, accurate forecast technology is the key to realize efficient and stable control. In this article, the performance optimization of the broadband large current feedback regulation system is studied, and a forecast model and feedback control strategy based on long-term and short-term memory network (LSTM) are proposed, aiming at improving the simulation running time and forecast precision of the system and reducing the false alarm rate. Through experimental verification, it is found that the improved system has obvious advantages in simulation running time and forecast precision. Compared with the traditional regulation methods and forecast models, the new method can better adapt to the complex and dynamic broadband large current feedback regulation environment and improve the stability and reliability of the system. In addition, the false alarm rate of the improved system has been effectively reduced, thus reducing the time and cost of system maintenance and repair. This research provides a new theory and method for the optimal design and control of the broadband large current feedback regulation system, and provides an important technical support and theoretical basis for promoting the development and application of the broadband large current feedback regulation system. In addition, this research can also provide useful reference and enlightenment for the optimization of other industrial process control systems, and is of great significance for promoting scientific and technological progress in the field of industrial process control.