Estimating moisture content in a fixed-bed grain dryer

Abstract This paper deals with a neural network application concerning to the determination of moisture distribution in an agricultural fixed-bed dryers. The aim of this study is to determine the influence on the different type of training and validation data used for neural network (NN) model. The validation of three kinds of data as constant, slow and fast were applied. The input data were generated an identified from a physically based model. It has been concluded that for satisfactory validation of an NN model different number of training data series should be linked together. Average deviation and maximum difference were used to estimate the influence of different training and validation input data.