CAJ | 학술논문

以玉米为原料进行挤压试验，开发基于双螺杆挤压的玉米膳食纤维改性的BP神经网络预测模型。此网络模型以螺杆转速、喂料速度、含水量和机筒温度为输入单元，以糊化度、吸水性（WAI）和水溶性（WSI）为输出单元，拥有一个8单元的隐含层。网络输出和目标输出之间的相关系数为0.98446，预测误差小于10%，具有较好网络性能，能够实现对玉米糊化度、吸水性和水溶性等挤压性能的预测。进一步开发人机交互图形化用户界面设计（GUI），方便预测模型的应用。
이옥미위원료진행제압시험，개발기우쌍라간제압적옥미선식섬유개성적BP신경망락예측모형。차망락모형이라간전속、위료속도、함수량화궤통온도위수입단원，이호화도、흡수성（WAI）화수용성（WSI）위수출단원，옹유일개8단원적은함층。망락수출화목표수출지간적상관계수위0.98446，예측오차소우10%，구유교호망락성능，능구실현대옥미호화도、흡수성화수용성등제압성능적예측。진일보개발인궤교호도형화용호계면설계（GUI），방편예측모형적응용。
Extrusion experiments are carried out using corn as the material to develop a back propagation (BP) neural network prediction model based on corn quality extruded by twin-screw extruder. This network model is to use the screw speed, feed rate, water content and temperature of the barrel as an input unit, the degree of gelatinization, water-absorbent index (WAI), and water-soluble index (WSI) as an output unit. It has an 8-unit of the hidden layer. The linear regression slope is 0.984 46 between the network output and the target output, and the prediction error is less than 10%. It shows that the network performs satisfactorily, and can be used to predict the degree of gelatinization, WAI and WSI of corn based on the network model. Further, graphical user interface (GUI) is developed to facilitate its application.