A neural network for predicting moisture content of grain drying process using genetic algorithm

This paper is concerned with optimizing the neural network topology for predicting the moisture content of grain drying process using genetic algorithm. A structural modular neural network, by combining the BP neurons and the RBF neurons at the hidden layer, was proposed to predict the moisture content of grain drying process. Inlet air temperature, grain temperature and initial moisture content were considered as the input variables to the topology of neural network. The genetic algorithm is used to select the appropriate network architecture in determining the optimal number of nodes in the hidden layer of the neural network. The number of neurons in the hidden layer was optimized for 6 BP neurons and 10 RBF neurons using genetic algorithm. Simulation test on the moisture content prediction of grain drying process showed that the SMNN optimized using genetic algorithm performed well and the accuracy of the predicted values is excellent.