BP人工神经网络在鱼糜挤压制品生产中的应用

Application of BP artificial neural network in extruded surimi product

采用反向传播(BP)人工神经网络和响应面法(RSM)模拟操作工艺参数(鱼糜含量、螺杆转速、III区加热温度)对双螺杆挤压机生产的鱼糜挤压制品的品质属性(持水性、膨润度、硬度和弹性)的影响,并比较了BP人工神经网络和RSM所建立的操作工艺参数与产品属性间关系模型的预测误差。试验结果表明,经训练的BP人工神经网络的模拟值和实际值的均方差(MSE)及和方差(SSE)均比RSM低,在模拟产品属性上具有更好的拟合度和准确性,采用此法确定的鱼糜挤压制品最佳工艺参数为:鱼糜含量45.70%,螺杆转速170r/min,III区温度106.2℃。

A back propagation （BP） artificial neural network （ANN） model was developed to predict the properties of extruded surimi products produced by a twin screw extruder. A BP-ANN model was established in MATLAB to simulate the relationships between running parameters of contents of surimi, screw speed and heating temperature of barrel III with the properties of surimi products such as water holding capability （WHC）, swelling degree （SD）, hardness （H） and springiness （S） during extrusion process. Using the experimental data from a quadratic general rotary unitized design, the neural network was trained and then validated with a validation subset. Besides, the method of response surface method （RSM） was also used to analyze and predict these properties. By comparing with mean squared error （MSE） and sum squared error （SSE） of BP-ANN and RSM models, it was showed that BP-ANN was more accurate than RSM in predicting the relationship between the responses and the running parameters. The BP-ANN was then used to search for a combination of running parameters resulting in maximal WHC, SD, S and minimal H. As a result, the optimal running parameters for content of surimi, screw speed and heating temperature was 45.70%, 170 r/min and 106.2 %, respectively.