Temperature Prediction of Yam under Infrared Drying Based on Neural Networks

Abstract:

Infrared drying experiments were carried out and the temperature data of yam were collected under different infrared intensities and infrared distances. The experiment results showed that the infrared intensity, infrared distance and drying time played an important role on the surface temperature and internal temperature of yam. Thus, infrared intensity, infrared distance and drying time were chosen as the input layers vectors of BP neural network model. A 3×9×1 single hidden layer BP network model was established. The model was trained by steepest gradient descent method and Levenberg-Marquardt algorithm respectively based on temperature data of yam. The maximum prediction error of optimized network model using Levenberg-Marquardt algorithm was 1.3℃, while the traditional algorithm of BP neural network was 5.7℃. It was indicated that Levenberg-Marquardt optimization method was superior to the steepest gradient descent method in the predicting temperature of yam with high precision. Therefore, it is feasible to predict temperature variations of materials during infrared drying process by using BP neural network model optimized by L-M algorithm.