In this paper, the principle of BP neural network is applied to the parameter identification process, combined with the traditional PID control algorithm to form an improved BP neural network PID control algorithm. This algorithm uses BP neural network to establish a system parameter model, which can track the changes of the controlled object and achieve higher identification accuracy. In view of the shortcomings of BP neural network that is sensitive to the initial value of the weight system, the initial weight coefficient of BP neural network is optimized. The BP network\'s own weight coefficient is corrected by BP algorithm to achieve online adjustment of PID parameters. The simulation results show that the algorithm has fast convergence speed, high accuracy, strong robustness and good stability, indicating the feasibility and effectiveness of the algorithm.
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