基于BPNN的黄酒前酵温度控制模型与仿真

Control strategy and simulation of fermentation temperature before yellow rice wine based on BP neural network

黄酒发酵过程十分复杂,且难以建立准确的数学模型,发酵温度自动控制一直是研究难点。传统PID控制器在前酵温度控制时,存在精度不高、响应缓慢和参数调节历时较长等问题,因此提出了基于BP神经网络的黄酒前酵温度控制策略,将BP神经网络应用于PID控制器的参数调节,改进了性能,并构建了动态仿真模型。仿真结果表明:设计的PID控制器控制精度更高、响应速度更快且有效改善了耦合干扰等问题。

The fermentation process of yellow rice wine is very complex,and it is difficult to establish an accurate mathematical model.When traditional PID controller is used for temperature control,the accuracy is not high,the response to problems is slow,and it need long time to adjust the parameters.Therefore,this paper proposes fermentation temperature control strategy before yellow rice wine based on the BP neural network,using the PID controller to adjust parameters,to improve the performance,and builds a dynamic simulation model.The simulation results show that the PID controller designed has higher control accuracy and faster response speed and the coupling interference is effectively improved.