目的 为了实现对啤酒杀菌强度准确的控制，设计一种基于改进BP多输入多输出（IBPMIMO）神经网络的温度设定模型。方法 采用IBMIMO神经网络对啤酒杀菌机主要温区的喷淋嘴温度设定值进行预测，并在啤酒杀菌机的杀菌强度（PU）控制软件上对预测值进行准确度分析。结果 IBPMIMO神经网络预测输出的各组喷淋嘴温度，最大综合误差为-1.09 ℃，而普通BP神经网络方法预测的结果综合误差波动较大，易陷入局部最优。并且将IBPMIMO预设的数据在杀菌机PU控制软件中运行，模拟结果显示，实验所测PU值与预期的PU值相差0.4。结论 利用IBPMIMO对杀菌机喷淋嘴参数的设定可以取得较好的效果，解决了新型啤酒瓶杀菌温度的设定。

The work aims to design a temperature setting model based on the improved BP multi input multi output (IBPMIMO) neural network to achieve the accurate control of beer sterilization. The temperature setting values of the spray nozzle in the main temperature zone of the beer sterilization machine were predicted by IBMIMO, and the predicted values were analyzed accurately by the PU control software in the beer sterilization machine. The maximum comprehensive error of the spray nozzle temperature predicted by (IBPMIMO) neural network was -1.09 ℃, while the result predicted by the general BP neural network method fluctuated greatly, and the local optimum was always got. Moreover, the preset data by IBPMIMO method were run in the PU control software of the sterilization machine, and the simulation results showed that the error between the PU value measured in the experiment and the expected PU value was only 0.4. Using IBPMIMO to set the parameters of the spray nozzle for the sterilization machine can achieve better results and solve the problem of setting the sterilization temperature of the new beer bottle.