Aiming to the problem that is very difficult to establish the mechanism model of quality for the process of tobacco leaves redrying, this paper proposes a quality prediction model based on principal component analysis (PCA) and improved back propagation (BP)neural network for tobacco leaves redrying process. Firstly, 12 input variables are confirmed by analyzing the factors on quality of tobacco leaves redrying process. Second, the methods of PCA is used to eliminate the correlation of original input layer data, in which 12 input variables are transformed into 6 uncorrelated indicators. Then, the quality prediction model based on improved BP neural network is established. Finally, a simulation experiment is conducted and the average prediction error is as low as 1.03%, the absolute error for forecasting is fluctuated in the range of 0.16% - 2.49%. The result indicates that the model is simpler and has higher stability for prediction, which can completely meet the actual requirements of the tobacco leaves redrying process.

To improve uniformity of quality in tobacco redrying process, an ANN-based method is proposed to predict the possible output of post-redrying. Firstly, this paper presents an intelligent process control model with ANN. And then, it describes the three-layers network and improves BP algorithm by using the method of variable learning rate, in which 10 process parameters were selected as inputs, the moisture and temperature of post-redrying as outputs, and one hidden with 25 neurons is designed. Finally, the ANN is trained to map the nonlinear relationship between inputs and outputs. With the analyses and comparisons, the result obtained shows that the prediction of possible output has higher accuracy by this improved ANN-based method. Such model designed can be suitable for quality improvement in the tobacco redrying process.