应用BP神经网络预测高压脉冲电场对果蔬干燥速率的影响

高压脉冲电场（HPEF）预处理果蔬可以提高果蔬干燥速率，改善干燥产品的品质，降低能耗。通过对白萝卜和苹果HPEF预处理干燥试验，探讨脉冲强度、作用时间和脉冲个数等预处理参数对干燥速率的影响。应用BP神经网络L-M训练法分析HPEF预处理参数与不同时间段干燥速率之间的关系。从而达到确定HPEF参数与不同时间段干燥速率之间因果关系的目的。研究结果表明，脉冲强度对干燥速率的影响比作用时间和脉冲个数对干燥速率的影响显著；建立白萝卜和苹果预处理干燥的BP神经网络仿真模型，并与实测值进行对比，用BP神经网络预测的干燥速率接近实测结果，具有较好的估计效果。

BP neural network prediction of the effects of drying rate of fruits and vegetables pretreated by high-pulsed electric field

The pretreatment of fruits and vegetables by high-pulsed electric field (HPEF) can increase the drying rate, improve the quality of dried product and reduce energy consumption. Through the HPEF pretreatment drying test of radishes and apples, the effects of pretreatment factors, such as pulse intensity, action time and pulse number on drying rate, were investigated. This paper analyzed the relationship between HPEF parameters and drying rate at different periods based on BP neural network of L-M training method, thus established the causation between them. The results show that the effect of pulse intensity on drying rate is more significant than the effects of action time and pulse number. The simulation model of BP neural network for pretreatment drying of radishes and apples was built and compared with the measured values. The drying rate predicted by the BP neural network is close to the measured values, so BP neural network can be used to estimate the result.