Modeling Of Temperature And Humidity In The Alfalfa Solar Drying Based On Artificial Neural Network

The drying method of alfalfa fixed thick layer is different from the thin layer drying. It has a highly complex, nonlinear process. Using traditional mathematical methods can not be established to reflect the deep solar drying process of alfalfa accurate model.Artificial neural network has good nonlinear mapping and a high degree of parallel processing of information capacity. Therefore this papers study the modeling of fixed deep alfalfa drying process with the artificial neural network. Select the key of temperature and humidity. The temperature and humidity prediction model will be established in alfalfa deep drying process. Mainly includes the following:1. By the alfalfa fixed deep solar drying test results, Analysis the relationship of temperature and humidity over time and temperature over humidity in alfalfa drying process. It contain the drying medium temperature and humidity, fan speed, the location of alfalfa, alfalfa initial moisture content that factors of alfalfa temperature and humidity changes.2. Modeling analysis of temperature and humidity of alfalfa solar drying based on artificial neural network. Determine the input vector of artificial neural network according to numbers of the temperature and humidity factors. Use MATLAB neural network toolbox, create the network structure. The experimental data obtained were divided into training set and testing data. Training the networks with the training data, choose training times less, high-performance network as the final network model. Simulating the model with the testing data, analysis the network performance, proving the feasibility of modeling and the model has the feature of high-precision and fitting.